Đặng Hoàng

Nguyễn Đình Quang

Đinh Văn Bình

Benoit Delinchant

Frederic Wurtz

Nguyễn Xuân Trường

Viện Khoa

Năng Lượng

Côngnghệ Việt

Hà Nội

RENEWABLE ENERGY SUPPLY (PV) INTEGRATION WITH BUILDING ENERGY MANAGEMENT: MODELING AND INTELLIGENT CONTROL OF ELECTRICAL STORAGE

Keywords: building energy management, electrical storage, renewable energy, demand response, energy autonomy

In building energy management, the electrical storage is important to ensure power supply continuity and reduce cost of electrical consumption. Therefore, an electrochemical battery model is highly recommended for our main objectives, which can contribute to simulate the impact of electrical storage in the building. In our framework, we have developed a complete solution for the electrical optimal management, including prediction, optimization, and real-time management of an electrical storage system with photovoltaic generation. We firstly present the models required to predict consumption patterns, production and storage. Then, under our experimental platform, we detail the predictive control algorithm, monitoring solutions and equipment control, as well as the results obtained. In near future, this research will be applied for the "Micro Smart Grid Development and Application for Building Energy Management" project (USTH, HIT, G2Elab and VAST).

INTRODUCTION

The building is a major sector in the power system and have an important role in solving problems related to the operation of power systems such as peak consumption and blackout. In addition to reducing consumption, reducing peak demand will go through the smoothing of daily consumption curves through actions of Demand Response (DR) as moving loads to times of low consumption [START_REF] Medina | Demand Response and Distribution Grid Operations: Opportunities and Challenges[END_REF] [START_REF] Kupzog | A closer Look on Load Management[END_REF].

Decentralization of energy production at the user level (especially in buildings) due to the integration of renewable energy, introduces additional difficulties in managing the overall production of electric network [START_REF] Hadjsaid | Dispersed generation impact on distribution networks[END_REF]. On the other hand, it can have degrees on consumption adjustment and energy autonomy.

The smart building must be equipped with the following capabilities related to: energy storage; energy consumption and load shedding; generation [START_REF] Siano | Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow[END_REF] [START_REF] Conejo | Real-Time Demand Response Model[END_REF]. These capabilities will be coupled with:  energy market , market offloading capability;  prediction: each node must be able to anticipate the capacity of production, storage and consumption.

 real-time control : each node must be able to dynamically adjust the production capacity , storage , consumption and load shedding to respond to hazards that may be related to incidents (loss of production units, meteorology, ... )

The storage medium can be heat capacity, it is indeed possible to anticipate heating using thermal inertia [START_REF] De Angelis | Optimal Home Energy Management Under Dynamic Electrical and Thermal Constraints[END_REF]. The storage medium can be electrochemical batteries, and in this case, the use of existing batteries is preferred. For example, vehicle-to-grid (V2G), operates the electric vehicle batteries [START_REF] Sortomme | Optimal Charging Strategies for Unidirectional Vehicle-to-Grid[END_REF] [START_REF] Sortomme | Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services[END_REF].

In our framework, we have developed a complete solution for the electrical optimal management, including prediction, optimization, and real-time management of an electrical storage system with photovoltaic generation. We firstly present the models required to predict consumption patterns, production and storage. Then, under our experimental platform, we detail the predictive control algorithm, monitoring solutions and equipment control, as well as the results obtained.

PREDICTION MODELS

In this section, model used for electrical energy prediction are briefly presenter (photovoltaic production, load consumption, electrical price). The energy storage systems, that we want to control, which is electrochemical batteries, have been modeled more accurately in order to have access to important parameters like the state of charge (SOC) and state of health (SOH), is detailed in section III.

Photovoltaic production

The power output of the PV generator is related with solar irradiance and ambient temperature. From these forecast data and manufactures data for PV modules, we can predict this production in according to the following equation [START_REF] De Angelis | Optimal Home Energy Management Under Dynamic Electrical and Thermal Constraints[END_REF]:

t m g PV G A N P   ( 1
)
where ηg is the instantaneous PV generator efficiency. This value is represented as follows [10]:

)) 1 )( 800 20 ( ) ( 1 ( pt r t t r a t pt r g NOCT G T T              (2)

Load prediction

From classroom internet calendar, we can obtain the occupant plan (1 or 0 for each hour) and number of students who will be using classroom computers for the next day. By supposing an average power load consumption of each used laptop of 30W, we can predict the load consumption (3) and its profile.

30 ns P load   (3)

Electrical price

In electrical market of developed country, the electrical price is depended on energy demand and furnished by electrical distributor (EPEX www.epexspot.com).

BATTERY MODEL

In simulation and application of electrical storage, the preferred electrical model of battery is electrical capacity, which is simple and describes energy balance in charging and discharging process. However, this model cannot describe accurately the functional states of the battery at each moment (state of charge, state of health) and some functional conditions (overvoltage, overcurrent) for predictive and feedback control. For example (Figure 1), in the constant voltage stage, charge current depends highly on battery property, and real charge time is much longer than estimated charge time by using an electrical capacity model. To reach these proposed goals and take into account the battery typical characteristics, a physical model is required but has to be as simple as possible.

Functional specification

Specification of this model is designed to fit a large scale of operations in different scenarios. The input data is the power set point (Psp: positive in discharge mode and negative in charge mode) and output data are real battery power (Pb), state of charge (SOC), state of health (SOH), Joule losses, available discharge power and available charge power. (Figure 2)

As a physical battery, the following constraints must be satisfied:

 Charge power should not cause overvoltage (V ≤ Vmax),  Charging current does not exceed a limit value (I ≤ Ilim).

In case of a discharge or charge power greater than the available value, the model will only provide its available power. Moreover, to preserve battery life-time, the model will decide to disconnect battery from electrical load when the state of charge is too low (SOC = SOCmin).

Electrical equivalent model

Charge current of a classic model

Charger float voltage of a classic model

Charge capacity of a classic model

Various models are available in the literature [START_REF] Guasch | Dynamic Battery Model for Photovoltaic Applications[END_REF] [17]to reach fine and fast simulation. In our framework, a simple model is preferred to describe charging and discharging process. This is the reason why we have chosen Shepherd's hypothesis [START_REF] Shepherd | Theoretical design of primary and secondary cells, part IIIbattery discharge equation[END_REF] as the basis content of this model. These hypotheses are based on a simple equivalent circuit: a voltage source is connected with a variable resistor. (Figure 3) This model must take into account the variation of battery voltage depending on battery state of charge. Indeed, the curve consists of three operating zones: exponential zone, nominal zone and polarization zone. (Figure 4) By synthesizing the three discharge phenomena and Shepherd's hypothesis, we can reestablish power discharging and charging equations [START_REF] Tremblay | A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles[END_REF].

For the discharge mode (IB ≥ 0), the battery power equation across the battery can be defined as below:

) Q Q ( B B B max B I B B max e I A I Q Q K I R I V P      2 2 0 (4)
In charge mode (IB ≤ 0), the polarization resistor is modified to approach the operation of the battery. So the power equation is rewritten:

) Q Q ( B B B max max B I B B max e I A I Q Q Q K I R I V P       2 2 0 (5)
These equations allow determining the state of charge (SOC), the available powers (charge and discharge) and Joule losses.

State of charge:

% 100   nom Q Q SOC (6)
Joule losses in discharge mode:

2 max 2 B B I I Q Q K I R losses Joule   (7)
Joule losses in charge mode:

2 max max 2 B B I I Q Q Q K I R losses Joule    (8)
Available discharge power:

) ( 4 ] [ max 2 )) ( 0 avaiable discharge_ max Q Q K R e I A V P I Q Q B B     (9) VOC RV VB IB Polarization zone

Renewable energy supply (PV) integration with building energy management: Modeling and Intelligent Control of electrical storage

Available charge power at constant current stage:

) ( lim 2 lim max max 2 lim lim 0 iable charge_ava max Q Q B I e I A I Q Q Q K I R I V P       (10)
Available charge power at constant voltage stage: [START_REF] Celik | Optimization and techno-economic analysis of autonomous photovoltaic-wind hybrid energy systems in comparison to single photovoltaic and wind systems[END_REF] Besides, the state of health (SOH) is estimated by "additive law" [START_REF] Long | A Practical Circuit based Model for State of Health Estimation of Li-ion Battery Cells in Electric Vehicles[END_REF]:

Q Q Q K R Ae V V V V P I Q Q B        max max ) ( max 0 max 2 max iable charge_ava max
% 100 1 max              Q N dt I SOH C B ( 12
)
The model can accurately simulate the behavior of an electric battery by using identified parameters from typical battery characteristics.

In our framework, we have to keep a compromise between accuracy and ease of use. In particular, model parameters can be considered constant in charge mode and discharge mode, thus facilitating the implementation of the model. Our model integrates the parameters for four famous kind of battery (Lead-acid, Ni-Cd, Ni-Mh, Li-ion).

Validation

Simulation in different modes

To survey available power in operation, the simulation test on Figure 5 illustrates set point powers for low and high demand for a battery with the following parameters: Ni-Cd, 4200mAh and initial SOC = 50%. Figure 5.a shows that the battery can provides a power that is lower than set point value (see zone A and zone B). Therefore, this demonstrates that our model allows calculating real battery power at the end of discharging or charging processes, this is important to decide the optimal control in building energy management. This model is validated by a test on a Laptop DELL Latitude E6400 including a Li-ion battery (rated voltage: 11.1 V, rated capacity: 6100 mAh, cycle durability: 1200, initial state of charges 100%). Measurements are obtained by BatteryMon software (v2.1, www.passmark.com).

Measurement vs Model 3.3.2.1 Simulation in different modes

In Figure 6, the simulation is well reproducing the measured power set point and the measured state of charge. Because of using average parameters for Li-ion battery, the model cannot reproduce exactly the battery voltage which is sensible with different parameter values. This has an influence for calculating the state of charge which is sometimes a little bit higher or lower than measurement data. This is also the case for the output power which cannot reproduce exact values at the end of discharge mode or charge mode as shown on Figure 6.a.

For health protection, the battery should not be used until the end of its charge like it was done in this test case. Thus the peak estimated voltage at the end of battery charge (see Figure 6.b) would not exist in almost operation modes. Thus, if we exclude this peak value, Figure 6.b shows that the difference between simulations and measurements is lower than 10% which is the level of accuracy that we can accept. Actually, the power set-point in charge mode of a laptop is not clearly defined. In fact, in almost cases, we usually estimate charge power of battery between the maximum power value of adapter and power consumption of the PC, while the real value depends completely on its properties (type of battery and technical parameters).

Validation in terms of charge mode:

In order to validate battery model function in this condition, we made a test of charge mode (without using the PC) of a Li-ion battery from a computer: DELL PRECISION module KY265 11.1 V, battery capacity: 85 Wh, design charge power: 130.65 W (19.5 V × 6.7 A), the initial state of charge SOC0 = SOCmin = 5%, maximum charge current Ilim = -0.7Qrat and maximum voltage Vmax = 1.13Vrat.

Zone of non-adapt

Renewable energy supply (PV) integration with building energy management: Modeling and Intelligent Control of electrical storage

In Figure 7, the estimated power curve during charging is close to the battery characteristic curve. In constant current stage, the model calculation could well reproduce the measured power. But, in constant voltage stage, battery model start to make errors in calculation and they will be accumulated and lead to a different at end of charge process with an order of 7%. In our framework, these errors of charge power and charge state are acceptable for the purpose of prediction.

IMPLEMENTATION IN "PREDIS SMART BUILDING"

The PREDIS "smart building" (PREDIS SB) is situated in Grenoble Electrical Engineering Laboratory (France). It is a building used for researches on (building + grid) smart system. Battery modeling and control is one of research activities of this project.

To realize application of battery control strategy, a part of this platform is used as a computer room with laptops and controllable switches. We simulate a photovoltaic generation supply for the 15 laptops with a curve of generated power for 1 day illustrated on Figure 8. A command server sends wireless ON/OFF signal to each switch to decide charging/discharging mode of each laptop. The goal is to reach, or to be closer as possible from the photovoltaic autonomy [START_REF] Kazema | Sizing of a standalone photovoltaic/battery system at minimum cost for remote housing electrification in Sohar, Oman[END_REF]. Therefore, our objective is to define an autonomous and optimal control strategy by using battery model from a proposed scenario and initial battery states.

Technical details:

 Laptop DELL Precision, battery module KY265, type Li-ion, 11.1 V, 85Wh  Photovoltaic panel: Pmax = 1000W.

Simulation using models for prediction and real-time control 4.1.1 Forecast data

Our first application is realized by a simulation on a typical day in 2012 November. The total load (Figure 8) is summed from wattmeter measurements of each laptop at 100% state of charge in order to retrieve pure user load consumption (without battery charge consumption). Besides, the photovoltaic production is taken from measured data (Figure 9) and the electrical price is given by EPEX www.epexspot.com (Figure 10). State of charge for each battery is initialized to 100% by a full charging process, but the optimal control tries to reach the target of 50% SOC at the end of each day in order to have the possibility to charge or to discharge batteries.

Dặng Hoàng Anh, Nguyễn Đình Quang

To reach an optimal management, the control strategy must be deduced by following two steps: predictive and feedback control. The predictive control calculates energy balance from data prevision and allows preparing necessary energy in battery for whole day operations. In this case, the battery model is used to define charge time and to decide when each laptop should be charged from electrical price evolution. Further, the feedback control decides what laptop can be charged or discharged in each check-point time (each 6 minutes). For this goal, model is also used to calculate or predict battery states at next time. Figure 11 shows total power consumption which is balanced with the photovoltaic production. By using batteries and an optimal control, the power consumption profile can benefit as much as possible from the generated power of the photovoltaic panel maximizing by this way the photovoltaic autonomy. Because the energy consumption in this case is more than the generated energy, this system need still to buy energy from electrical grid which is stored on laptop battery.

Simulation results

From Figure 12, the necessary power is bought at the cheapest moment (3:00am to 5:00am, from electricity variable prices of Figure 10) to minimize costs. Sometime, we can sell generated power to grid but in reality, this power is used locally for other consumptions (HVAC, lighting…). To understand the gains of this application, the table below compares our strategy to the classical case (PC without electrical storage). The cost gain is about 10 times.

Application in real the system

The previous part was done for a simulated system in order to developed models and optimal control algorithm. In this section, we have applied the optimal control on the real system.

Control devices

In this section, we introduce control materials in our control protocol: command server, receiver and transmitter RF module, controlled outlet, Zigbee wattmeter and virtual sensor (Figure 13).

By integrating the control module and the management module data, our home abstraction layer (HAL) server allows retrieving measured data and control actuators in order to manage energy devices and especially energy storage system of laptops. Renewable energy supply (PV) integration with building energy management: Modeling and Intelligent Control of electrical storage

Besides, receiver and transmitter RF module communicates to command server on TCP / IP connection. This module converts RF frames to PLC signals (power line communication) which are transferred on the electrical grid, in order to turn on or off the controlled outlets.

Zigbee wattmeter and controlled outlet are inserted between the network electrical and laptop adapter (Figure 14)  Zigbee wattmeter measures power consumption and sends it via Xbee gateway, which is connected to the server by USB interface port.

 The controlled outlet receives ON/OFF signals via electrical grid by PLC.

Measured battery status (SOC, SOH ...) is got from software BatteryViewInfo (Figure 15) (www.nirsoft.net).

This software allows exporting data in several types (*. Txt, *. html*. xml ...), which are very easy to operate through the TCP/IP network. Thus, this software is considered a "sensor virtual " because it produces quantities that are not directly measured such as current capacity (%).

Real system results

The optimal controller has been installed into the command server and is able to call model simulation for prediction phase, as well as real-time monitoring and control using HAL server. Results obtained during a day are the followings.

In Figure 16, we can notice that batteries charging have been realized early on the morning and that power taken from electrical outlet follows photovoltaic production. In Figure 17, we can see batteries charging and discharging power, as well as computers load. Fig. 18, is detailing batteries state of charge (SOC). Before class starting, batteries are full of charge. At 8 o'clock, some PC start running discharging their battery. After some minutes, photovoltaic production is enough to supply energy to some PC. From 11 am to 2 pm, there is an amount of energy which supply computer load as well as charging of batteries. From 2 pm to 6 pm, batteries are discharging due to insufficient production. At the end of the day, batteries SOC are very low which is due to an intensive use of computer during.

RESULTS AND

Based on ideas and results of this research, under the cooperation between USTH, HIT, G2Elab and IES-VAST, we are deploying the "Micro Smart Grid Development and Application for Building Energy Management" project in order to approach two new research directions: building energy management and micro smart grid [START_REF] Delinchant | Micro Smart Grid Development and Application for Building Energy Management[END_REF]. In fact, our research focuses on the Micro Smart Grid development at the local level (building) that is estimated to last for a period of 3 years (2015 -2018). The 5th floor and 6th floor (with four labs and a classroom) of USTH building, where we can carry out experimental simulation model in a real case, have been chose to be the research platform. Besides, the power supply system will be from a photovoltaic energy source, an electrical storage (battery energy storage station -Lithium batteries blocks of LiFePO4), and EVN power grid (low voltage). Indeed, the photovoltaic generation system at power scale of 15 kWp including an inverter and solar panels will be installed on the rooftop of USTH building. This PV system extracts maximum power from the PV arrays and injects maximum power into grid in various conditions. The load includes in this case single phase and triple phase namely lighting systems, heating ventilation and air-conditioning systems (HVAC systems), and elevator of USTH building. Moreover, we will utilize another bank called "load bank". It allows the simulation of resistive, inductive and capacitive loads and provides for the research in different conditions in the smart grid platform. Besides that, the monitoring system (energy meters, PLCs and SCADA) allows collecting data that can be analyzed providing information for the optimal operation. The energy controller reads all the data measured by the transducers for managing and running the equipment following the different selected modes. The energy manager controls the delivery of energy, the run of charges and discharges batteries.

NOMENCLATURE

) ( 0 OC max V Q Q B e A V    Open circuit voltage (V) Q Q K R R I V max

Figure 1 .

 1 Figure 1. Charge profile of a Li-ion battery

Figure 2 .

 2 Figure 2. Battery model specification.

Figure 3 .

 3 Figure 3. Electrical equivalent circuit.

Figure 4 .

 4 Figure 4. Typical Discharge Curve Characteristics.

Figure 5 .

 5 Figure 5. Power and state of charge in different modes.

  Dặng Hoàng Anh, Nguyễn Đình Quang

Figure 6 .

 6 Simulations vs measures for power, voltage and SOC.

Figure 7 .

 7 Figure 7. Model validation for power in charge mode.

Figure 8 .

 8 Figure 8. Total load consumption by laptop device.

Figure 9 .Figure 10 .

 910 Figure 9. Photovoltaic power.Figure10. Electrical price.

Figure 11 .

 11 Figure 11. Total power consumption at electrical outlet and photovoltaic power.

Figure 12 .

 12 Figure 12. Electrical grid power.

Figure 13 .

 13 Figure 13. Communication network.

Figure 14 .

 14 Figure 14. Electrical power meter and receiver (Zigbee radio frequency).

Figure 15 .

 15 Figure 15. Battery monitoring tool

Figure 16 .Figure 17 .

 1617 Figure 16. Photovoltaic production ant electrical outlets power

Figure 18 .

 18 Figure 18. Laptops battery state of charge on real system

Acknowledgements. The research was realized in Grenoble Electrical Engineering Laboratory, under the supervision of Prof. Benoit Delinchant and Prof. Frederic Wurtz.


Olivier Rousse
email: olivier.rousse@univ-grenoble-alpes.fr.

Benoît Sévi
email: benoit.sevi@gmail.com.

Informed trading in oil-futures market *

Keywords: JEL Classification: G13, G14, Q4 insider trading, WTI crude oil futures, intraday data, inventory release

The weekly release of the U.S. inventory level by the DOE-EIA is known as the market mover in the U.S. oil futures market and to be a significant piece of information for all world oil markets in which the WTI is a price benchmark. We uncover suspicious trading patterns in the WTI futures markets in days when the inventory level is released that are higher than economists' forecasts: there are significantly more orders initiated by buyers in the two hours preceding the official release of the inventory level. We also show a clear drop in the average price of -0.25% ahead of the news release. This is consistent with informed trading. We also provide evidence of an asymmetric response of the oil price to the news, and highlight an over-reaction that is partly compensated in the hours following the announcement.

Introduction

This paper provides evidence of suspicious trading patterns consistent with informed trading in the WTI oil-futures market on the days when the announcement of the U.S. crude oil stock level by the Department of Energy contrasts with the expectations of energy analysts collected in Bloomberg's inventory survey. Our results reveal significant order imbalance, with a majority of buyer-initiated trades in the two hours preceding the announcement of positive surprises -changes in inventories that are larger than expected -at 10:30 a.m. on Wednesday. 1 The findings are robust to alternative definitions of the surprise and the measures of order imbalance considered. Our results have important implications, as the WTI futures market is the leading market with respect to price discovery (see the recent evidence in [START_REF] Elder | Price discovery in crude oil futures[END_REF]) and the most-traded futures commodity contract worldwide.

The inventory level is known to be a central variable for the determination of oil prices, as highlighted in early theoretical contributions such as [START_REF] Deaton | Competitive storage and commodity price dynamics[END_REF], [START_REF] Pindyck | Inventories and short run dynamics of commodity prices[END_REF][START_REF] Pindyck | The dynamics of commodity spot and futures markets: A primer[END_REF], [START_REF] Geman | Soybean inventory and forward curve dynamics[END_REF], [START_REF] Pirrong | Stochastic fundamental volatility, speculation, and commodity storage[END_REF] or more recently Kilian and Murphy (2013), [START_REF] Kilian | Quantifying the speculative component in the real price of oil: The role of global oil inventories[END_REF], [START_REF] Smith | The informational role of spot prices and inventories[END_REF] and [START_REF] Knittel | The simple economics of commodity price speculation[END_REF]. As noted in Kilian and Murphy (2013): "[...] any expectation of a shortfall of future oil supply relative to future oil demand not already captured by flow demand and flow supply shocks necessarily causes an increase in the demand for above-ground oil inventories and hence in the real price of oil." (p. 455) Inventories may also be related to precautionary demand, as highlighted in [START_REF] Alquist | What do we learn from the price of crude oil futures?[END_REF] and Hamilton (2009a). 2 In a world of scarcity of fossil fuels, the role of inventories might well be exacerbated (see Hamilton (2009b)).

It is hence not surprising that weekly announcements about the level of oil stocks in the U.S. are 1 All times in the paper are eastern standard times (EST). 2 Hamilton points out that: "One might also try to measure the contribution of precautionary demand by looking at changes in inventories. [...] inventories were going down, not up, at the time of the sharpest price movements, suggesting that inventory changes were serving to mitigate rather than aggravate the price shocks. Positive inventory investment typically came much later, as firms sought to restock inventories that had been earlier drawn down. " (p. 223) 1 eagerly anticipated by the financial community 3 and their absence -for instance in the case of the 2013 government shutdown -can be viewed as "darkness" for traders. 4 As shown in recent research, changes in inventory levels can have a significant impact on oil prices, as in [START_REF] Bjursell | Inventory announcements, jump dynamics, volatility and trading volume in U.S. energy futures markets[END_REF] and [START_REF] Elder | Jumps in oil prices: The role of economic news[END_REF]. 5 Going beyond the oil market, it is well-known that the release of the oil-inventory level each week affects the volatility of the stock index. 6 This is to be expected in the light of the empirical results in [START_REF] Jones | Oil and the stock markets[END_REF] and Kilian and Park (2008), who both show how oil price shocks spread into the stock market. The release of inventory level might also affect the currencies of oil-exporting countries, as the oil price has been shown to be a predictor of exchange rates in these countries [START_REF] Ferraro | Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates[END_REF], [START_REF] Zhang | Exchange rates and commodity prices: Measuring causality at multiple horizons[END_REF]).

This impact of the EIA announcement release on oil prices is illustrated in Figure 1, where we plot the transaction prices over the day of July 16th 2008. The Bloomberg median forecast for this day was a drop of 2.2 million barrels, with individual forecasts by oil experts going up to -3.9 million barrels. The actual reported value was instead a rise in stocks of 2.952 million barrels, which greatly surprised the market and led to a drop in price of almost 6 dollars in the few minutes following the release at 10:35 a.m.

Apart from inventory announcements, there has been substantial research on the impact of nonoil-specific news on oil prices. For instance, [START_REF] Barnhart | The effects of macroeconomic announcements on commodity prices[END_REF] is an early attempt to assess the reaction of commodity prices to macroeconomic announcements. Using daily data, [START_REF] Kilian | Do energy prices respond to U.S. macroeconomic news? A test of the hypothesis of predetermined energy prices[END_REF] find no evidence of an impact of macro news on oil prices. This has important implications for the exogeneity of oil prices and its and its ordering in multivariate models such as vectorautoregressive models when the impact of shocks is investigated. [START_REF] Chatrath | Does the price of crude oil respond to macroeconomic news[END_REF] confirm these findings using intraday data. [START_REF] Rosa | The high-frequency response of energy prices to U.S. monetary policy: Understanding the empirical evidence[END_REF] and [START_REF] Basistha | The impact of monetary policy surprises on energy prices[END_REF] analyze the impact of monetary surprises on oil prices using intraday data. As such, they can identify the effect of Wall Street Journal (2015). Bloomberg (2013). Similar evidence for the gas market appears in [START_REF] Chiou-Wei | The response of U.S. natural gas futures and spot prices to storage change surprises: Fundamental information and the effect of escalating physical gas production[END_REF]. In a related study, Linn and Zhu (2002) onsider the volatility surrounding the release of the gas-storage report. Wall Street Journal Online (2015).

Figure 1

The oil price on July 16th 2008. The oil-inventory announcement was higher than expected.

surprises at the exact time when they occur. [START_REF] Datta | Generating options-implied probability densities to understand oil market events[END_REF] provide evidence that events of various types can significantly affect on the conditional distribution of returns measured by the option-implied density. [START_REF] Berk | Regulatory interventions in the U.S. oil and gas sector: How do the stock markets perceive the CFTC's announcements during the 2008 financial crisis?[END_REF] investigate the impact of CFTC announcements on oil prices.

It is well-known, however, that the largest impact on oil prices comes from the inventory announcement, which is the most anticipated piece of news in the oil market. Recent research has thus focused on the impact of this release of the stock level. [START_REF] Bu | Effect of inventory announcements on crude oil price volatility[END_REF] is one attempt to assess the impact of inventory announcements on both oil prices and oil volatility. This work, however, uses daily data, a frequency at which the phenomena taking place over a few minutes might well not be visible.

Halova Wolfe and Rosenman (2014) also consider the impact of the oil-inventory announcement on both oil and gas prices and volatility. They use intraday data, but focus on the specific time of announcements, thereby ignoring the periods around the time of the release. [START_REF] Bjursell | Inventory announcements, jump dynamics, volatility and trading volume in U.S. energy futures markets[END_REF] also make use of intraday data to investigate the association between inventory announcements and price jumps, and provide evidence that these, mainly large jumps, often appear at the time of the stock announcement. [START_REF] Elder | Jumps in oil prices: The role of economic news[END_REF] also use intraday data to detect intraday jumps in oil prices. The authors provide evidence that jumps often coincide with the release of either macro-news or oil-specific news (inventories), showing the importance of fundamentals in the determination of oil prices beyond speculative motivations. 7 Finally, [START_REF] Ye | The informational content of inventory announcements: Intraday evidence from crude oil futures market[END_REF] consider the informational content of inventory announcements, looking at both EIA and API releases over the short August 2012 -December 2013 time period. They find no significant, or at best a weakly significant, price impact of the API is and that the market mover for oil is indeed the EIA release.

In contrast to our study, the authors only focus on the impact of the news and not on potentially suspicious trading patterns before the official release time. 8 Our work takes a different approach, building on recent contributions by [START_REF] Irvine | Tipping[END_REF][START_REF] Christophe | Informed trading before analyst downgrades: Evidence from short sellers[END_REF], [START_REF] Blau | Informed or speculative: Short selling analyst recommendations[END_REF] and [START_REF] Bernile | Can information be locked-up? Informed trading ahead of macro-news announcements[END_REF], where the focus is on the detection of information leakage before official announcements. 9 In contrast with previous work such as [START_REF] Ederington | The short-run dynamics of the price adjustment to new information[END_REF], these papers go beyond the simple analysis of return patterns to assess the possibility of leakage by taking into account order imbalance as a symptom of informed trading.

We also base our analysis on order imbalance to draw conclusions about the likelihood that market participants trade on private information.

The impact of the arrival of information on stock returns is a fundamental and recurrent question in financial economics. Under the strong form hypothesis of the Efficient-Market-Hypothesis Hamilton (2009a and b), [START_REF] Büyükşahin | Do Speculators Drive Crude Oil Futures Prices?[END_REF], [START_REF] Alquist | The role of financial speculation in driving the price of crude oil[END_REF], [START_REF] Singleton | Investor flows and the 2008 boom/bust in oil prices[END_REF], [START_REF] Knittel | The simple economics of commodity price speculation[END_REF] and [START_REF] Bunn | Fundamental and financial influences on the co-movement of oil and gas prices[END_REF] are examples of contributions discussing the relationship between oil prices and speculation.

As noted in [START_REF] Fattouh | The role of speculation in oil markets: what have we learned so far?[END_REF], the conclusions from these analyses of commodity financiarisation are mixed and do not support the view that speculation unambiguously causes oil prices. There is similar work using intraday data for non-energy commodities such as corn [START_REF] Lehecka | Gone in ten minutes: Intraday evidence of announcement effects in the electronic corn futures market[END_REF]). The focus is again on the price reaction to scheduled announcements, while we look at both the price and the trading pattern in the pre-announcement period. Information leakage has been investigated in various contexts, such as credit derivatives [START_REF] Acharya | Insider trading in credit derivatives[END_REF]), sovereign-debt ranking [START_REF] Michaelides | The adverse effects of systematic leakage ahead of official sovereign debt rating announcements[END_REF]), tender offers [START_REF] Jarrell | Stock trading before announcement of tender offers: Insider trading or market anticipation[END_REF]), SEC filings [START_REF] Rogers | Run EDGAR Run: SEC Dissemination in a High-Frequency World[END_REF] and supervised industries [START_REF] Reeb | Insider trading in supervised industries[END_REF]). Paradoxically, the last paper shows that information flows to the regulator lead to greater levels of insider trading.

(EMH), it is not possible for even an insider to outperform the market portfolio. Under the semistrong form hypothesis, which is more plausible, a trader may benefit from private information if this latter produces a valuation that is different enough from market expectations for the asset of interest. The extensive literature on the EMH following Fama (1970) has recently been reinvigorated by the availability of very high-frequency trading data and the possibility of relating news to intraday returns. Early attempts such as Ederington andLee (1993, 1995) found that the price changes following news releases are often completed in the first seconds after the announcement.

Many other contributions followed that have helped to enhance our understanding of how prices incorporate information. 10Our paper is the first to focus on the possibility of information leakage before the inventory announcements, and to show that the trading patterns are consistent with informed trading. We specifically examine the trading activity around the weekly inventory announcements in the frontmonth WTI oil-futures contract traded on the NYMEX-CME over the 2007-2014 period. The U.S.

Department of Energy makes an announcement about the level of oil inventories each Wednesday at 10:30. We investigate potential trading by informed investors in the hours preceding the official news release. We use intraday data to calculate the order imbalance over short intervals (2 or 5 minutes) and show that there is significant order imbalance in days when the news release contains surprising inventory-level information. This pertains when the actual stock level is higher than expected (a positive surprise). The bulk of the order imbalance occurs around the beginning of the open outcry trading session when liquidity is sufficiently high.

Our results can be taken as providing preliminary evidence that the inventory level released by the DOE each Wednesday is known by some market participants who are able to benefit from their insider position to make money with the news. More generally, our results call into question the overall informational efficiency of the most liquid commodity market in the world. We should, nevertheless, be careful in interpreting these findings. While our results are consistent with the presence of informed trading, they do not explicitly demonstrate it. In particular, some traders may have superior ability in either predicting the inventory level to be released and/or in analyzing the ongoing information flow about oil supply and demand conditions. 11

The rest of the paper is organized as follows. The next section reviews the issue of insider trading in the specific case of commodity markets. Section 3 presents the price and inventory data and provides preliminary evidence of the diffusion of private information on the oil price before the official release time. Section 4 is then devoted to the empirical analysis, where order-imbalances are formally related to surprises. The last section concludes and discusses the implication of our findings, as well as avenues for future research. [START_REF] Grossman | An analysis of the role of "insider trading" on futures markets[END_REF] theoretically discusses the potential interest of insider trading in futures markets.

Insider trading in commodity markets

On the one hand, insider trading can be liquidity-enhancing but can also, on the other, reduce liquidity and markedly affect the viability of futures markets. [START_REF] Leland | Insider trading: Should it be prohibited?[END_REF] corroborates these effects, adding that insider trading does help to integrate information into prices. Outside investors, nevertheless, can lose significantly. [START_REF] John | Insider trading around dividend announcements: Theory and evidence[END_REF], building on early work by [START_REF] Pettit | Dividend announcements, security performance and capital market efficiency[END_REF], analyze the impact of earnings announcements in the stock market.12 More recently, [START_REF] Hirshleifer | Security analysis and trading patterns when some investors receive information before others[END_REF] and [START_REF] Brunnermeier | Information leakage and market efficiency[END_REF] have developed theories of the utilization of private information in trading. and conclude that short-lived informational advantages can lead to significant profits for insiders.

These high and unfair potential profits are naturally a source of concern for regulators. Indeed, "in-11 Some traders may benefit from better due diligence, which is now very sophisticated. For instance, [START_REF] Verstein | Insider Trading in Commodities Markets[END_REF] reports the use of helicopters with infrared cameras flying around storage facilities in Oklahoma in order to estimate oil reserves. sider trading" is generally regarded as an illegal activity. However, this term covers both illegal and legal trading activities, depending on the nature of the market (equities, commodities) and regional trading rules. In equity markets, legal insider trading refers to corporate insiders (officers, directors or employees) trading their own securities within the boundaries of company policy and regulations governing overall trading. For instance in the U.S., corporate insiders must report their trades to the SEC. Insider trading in equity markets is broadly prohibited in all countries with stock markets. Insider-trading laws were first established in the United States (1934), then in France (1967) and finally appeared much later in some developed countries such as Germany (1994). 13In commodity-futures markets, the illegal nature of insider trading is less obvious than in equity markets. It is clear that having private information can give a trader an unfair advantage in commodity futures markets. If the trader hears about an important event before the public, he holds a short-lived information advantage from which he can possibly profit by for instance selling short in the hope of buying more cheaply later. But it is not because this is unfair that it is illegal. Actually, insider trading using misappropriated governmental information remained legal until 2010, and insider trading on information obtained outside of the government is still allowed on commodity futures markets. The reason for the latter is that futures markets are hedging markets: their purpose is to protect buyers and sellers from unexpected price movements which represent commercial risk. As hedging decisions are linked to commercial positions that potentially affect commodity prices, it seems difficult to prohibit insider trading on information obtained outside of the government. In other words, the commercial risks depend on material information that hedgers hold. As such, the prohibition of insider trading would be in opposition to the essential nature of commodity futures markets. This difference between securities and commodity futures probably explains why the U.S. only recently banned insider trading using non-public information from government sources in commod-ity futures market, with the approval of the U.S. Financial Reform Law in July 2010. 14 This post-2008 reform includes a section dealing with insider trading using data on commodities that practitioners used to coin the "Eddie Murphy rule". 15 This section of the 2010 Financial Reform Law illustrates the effort to standardize CFTC and SEC rules, but received a cold reception from commodity traders for two main reasons. First, they were concerned that the wording of the Law would prevent them from trading on their own information that they had previously reported to the government (USDA, DOE and CFTC) and which was not yet released. Second, they fear that government analysts would be prevented from answering questions from private-sector analysts and traders. Due to the nature of commodity futures markets, the proposed legislative text has been tweaked in order to prevent curbs on information exchange.

Despite there being no historical evidence of USDA or DOE leaks in recent decades, the "Eddie Murphy" rule serves the market and public by protecting market participants and promoting market integrity. There have, however, been past experiences of insider-trading scandals in commodity markets. The most notable one dates back to 1905 and involved a trader and a USDA statistician.

Since this scandal, weekly and monthly report preparation has been increasingly secured by the USDA: locked doors, closed window shades, armed guards, and criminal penalties for staff who leak. The EIA has also adopted increasingly strict procedures since the last mistake of May 29th 2008. At that time, the oil price was $130 a barrel and the EIA briefly released its weekly inventory report online before the scheduled time. Warned by web robots, informed traders profited from this mistake and the data quickly spread via instant messages. The EIA website was recording 5,000 hints a second around the time of the report release. In the end, no traders were punished as this was a mistake by the EIA.

According to some analysts, the probability of information leakage from government offices such like the USDA or the DOE is extremely slim (Futures Magazine, 2010). However, knowing that some Fed officials were recently concerned by the suspicious use of insider information (Wall Street Journal, 2016), information leakage from the DOE remains, in the light of our results, a credible phenomenon.

Data and preliminary data analysis

Our empirical analysis requires two types of data: information on inventory announcements and market expectations, and then oil-futures prices at high-frequency. Our choice of the January 2007 -October 2014 period is dictated by the electronic trading in Globex from July 2006 onwards, thereby making the nature of the market very different from this date. We thus decided to exclude the period before January 2007.

Inventory announcements, expectations and surprises

Information on the inventory level is generally released each Wednesday at 10:30 a.m., except when the Wednesday corresponds to a holiday -in which case the announcement is made on the Thursday or possibly the Friday. The release is made by the U.S. Energy Information Administration (EIA), which is the statistical and analytical agency within the U.S. Department of Energy (DOE). This weekly announcement is coined the "Weekly Petroleum Status Report" (WPSR), and includes many figures relating to the oil market such as field production, imports and exports, inputs and production at refineries and blending terminals, production from gas-processing plants and fractionators, and inventories at refineries, terminals, pipelines, and fractionators. The report covers the 50 States and the District of Columbia. 16 We here only consider the change in the total petroleum stocks for the U.S., excluding Strategic Petroleum Reserves. This change in stocks is calculated using com- pany submissions for the week ending at 7:00 a.m. the preceding Friday.

It is important to note here that the EIA does not pre-release/embargo this data to any outside entities. 17 As such, our case similar is different from that in [START_REF] Bernile | Can information be locked-up? Informed trading ahead of macro-news announcements[END_REF], where Federal

Open Market Committee (FOMC) scheduled announcements are pre-released to media companies for analysis before public release. Our investigation then covers a longer period -several hoursbefore the announcement and is not confined to any predetermined period.

We define surprises using the weekly Bloomberg's survey that is conducted with the participation of a dozen experts in the oil industry. For the period February 7th 2007 to October 22nd 2014, we have data on 402 announcements with a mean number of experts of 13.14. The minimum and maximum number of experts is 8 and 19, respectively, making the calculation of statistical values feasible.

Over our study period, 55 analysts representing 49 firms that are invested in oil trading delivered forecasts. We also collect the exact time release, the date and individual forecasts from experts from the Bloomberg database. 18 This allows us to measure the gap between the prediction and the actual 17 We thanks employees at the EIA for confirming this point. Our results about price and trading patterns before announcements are then not the product of possible leakage due to pre-release. 18 In a few instances, the release is made at 10:35 a.m. or 11:00 a.m. We take this into account in our analysis and analyze price and trading relative to the announcement time. In some Figures, we report time rather than time relative to news release for clarity. The reader should be aware that in the case of delayed announcement, the reported time is not exact.

value, and thus whether to classify an announcement as a surprise. Table 1 reports the summary statistics for the actual values, forecasts and forecast errors. As can be seen, the forecast is, on average, slightly optimistic and the variability of realized values and forecasting errors is large.

We rely on these survey forecasts, as there is no traded instrument that would allow us to infer market expectations (as in the case of interest rate futures for FOMC meetings). In our baseline analysis, we define a surprise as an actual value that is over three standard deviations from the median forecast in the Bloomberg survey: we call these 3σ surprises. 19 At the end of the empirical section, we show that our results are robust to alternative definitions of surprises.

Table 2 shows the summary statistics for surprises. In the total sample of 402 announcements over the February 2007 -October 2014 period, there are 90 surprises (22.39%), of which 43 are positive and 47 are negative. This is sufficient to carry out a statistical analysis of surprises. The number of yearly surprises in Table 2 is quite stable, as is the ratio of yearly positive to negative surprises. Each year, there are 3 to 8 positive or negative surprises. We also do not see any particular trend over the sample, with surprises not being clustered in particular years. To further examine the occurrence of surprises, Figures 2 and3 respectively plot the number of surprises and the number of positive vs. negative surprises by week number. Again, there is no particular pattern: surprises do not appear in certain periods of the year, and neither do positive or negative surprises. We thus conclude that the econometric analysis does not need to consider year or week effects, or any seasonal correction, to take into account particular time patterns in surprises, despite the well-known seasonality in inventory levels over the year.

An alternative to the EIA release is the inventory news reported by API every Tuesday afternoon at 4:30 p.m. 20 The API announcement takes place before the EIA release but after the experts have 19 Our definition is different from that in [START_REF] Halova | Noisy inventory announcements and energy prices[END_REF]. The latter define a surprise as the difference between the actual value and the median forecast, while we define the surprise as a categorical variable which takes the value one in case of a surprise and zero otherwise. In other words, while [START_REF] Halova | Noisy inventory announcements and energy prices[END_REF] estimate the magnitude of the surprise we focus on whether the announcement is a surprise or not. 20 If Monday is a Federal holiday, the reports are released on the Wednesday afternoon.

5.47%

The table shows the sample statistics for oil-inventory surprises (SUR) over the period February 7th 2007 to October 22nd 2014. SUR refers to the actual inventory announcement being +/-3 or 4 standard deviations away from the median forecast of the weekly Bloomberg survey. A positive surprise (PosSUR) is defined as a forecasting error over +3 or +4 standard deviations and a negative surprise (NegSUR) as a forecasting error over -3 or -4 standard deviations. NoSUR refers to all the other cases where the actual inventory is in the range between the lowest and highest economists' estimates, or not much over this range.

been interviewed by Bloomberg to carry out its survey. As such, the informational content of the API report might affect the surprise or non-surprise characteristic of the EIA announcement the next day. This may not very likely, as recent research [START_REF] Ye | The informational content of inventory announcements: Intraday evidence from crude oil futures market[END_REF]) has found that the API report has only little informational content. 21 Note that, in addition, any informational content in the API report would mean that some of the announcements that we categorize as surprises are in fact not so, thereby having a marginal effect on our overall results.

WTI futures prices

Our price data are the transaction prices for the WTI futures (ticker: CL) quoted on the NYMEXpart of the CME Group from 2008 onwards -for the period covering all the announcements. Futures contracts used to be traded both on the electronic market -electronic trading began in July We choose WTI futures for two reasons. First, WTI futures continue to be the world benchmark for crude oil [START_REF] Elder | Price discovery in crude oil futures[END_REF]), although this status has been called into question by recent research using high-frequency (intraday) data (see [START_REF] Liu | Price dynamics in global crude oil markets[END_REF] and the references therein). Second, together with Brent traded on the ICE, the WTI today is the world's most liquid crude oil contract (both contracts trade around 150 million units a year) and was twice as liquid as the ICE-Brent over the 2007-2014 period (see Halova Wolfe and Rosenman ( 2014)). This is also the most-traded commodity future over the world in value. This liquidity is also visible in the number of daily transactions. Over our analysis period, the daily number of transactions on the front-month contract is about 90,000, ensuring reasonable liquidity at all times of the day. 24 As such, WTI futures should attract informed traders who are strongly incited to trade in deep markets to limit the price impact of their orders -if the transaction has a significant impact on the market price, the informational advantage could be unprofitable -and to enjoy low transaction costs. We then consider WTI futures for their liquidity and the ability of most traders to hold positions at competitive cost in this market.

As is common for the analysis of the futures prices of a given commodity, we use a continuous series of futures prices by considering the front-month futures contract and switching to the next front-month contract once the latter's trading volume exceeds the former. As such, we are certain to look at the most liquid WTI contract available for trading in the market at every point in time.

In addition, the front-month contract should be the quickest to reflect available information and to react to new information, thereby being the most desired way in which informed traders can benefit from private information.

To gauge liquidity in days with announcements, Figure 4 the time of the news release and slowly-decreasing trading activity ending with a last period of relatively intense trading corresponding to the final minutes of open outcry. 25 The huge number of traded contracts at the time of the announcement shows the importance of the EIA release in resolving the uncertainty surrounding the inventory level. We can also see from Figure 4 that volume is, on average, higher on days with surprises, as market participants trade in response to the new inventory level that has just been released to adjust their positions.

The return pattern around announcements

Our first analysis of the data focuses on the return patterns of WTI futures series in days with inventory announcements. Figure 5 plots the average cumulative returns from 6:30 a.m. to 2:30 p.m., distinguishing between days with no surprise, days with a 3σ positive surprise and days with a 3σ negative surprise. A number of comments can be made. First, there is no particular pattern in days with no surprise, except for a small fall in price at the time of the announcement and during the two hours before the announcement. This can be explained by our definition of a surprise, which is not perfect by nature. An inventory level in line with expectations may surprise market participants whose forecasts are different from the consensus. The negative reaction of the oil-futures price in no-surprise days is due to an asymmetric feature that we will elaborate on below: the impact of positive surprises is much larger, on average, than that of negative surprises.

Second, in days with positive surprises we observe a clear run-up in returns starting at around 8:30 a.m., i.e. two hours before the official release of the EIA inventory level. The pattern is then similar to that in [START_REF] Hendershott | Are institutions informed about news?[END_REF] and [START_REF] Bernile | Can information be locked-up? Informed trading ahead of macro-news announcements[END_REF], who also find increasing cumulative returns ahead of news. At the time of the announcement, this anticipation leads to an average fall in returns of 0.25%, which is to be compared with the 0.05% figure in [START_REF] Bernile | Can information be locked-up? Informed trading ahead of macro-news announcements[END_REF] before the FOMC announcement. 26 Our result is in line with [START_REF] Ye | The informational content of inventory announcements: Intraday evidence from crude oil futures market[END_REF], who also identify large price variations around surprising announcements.

Third, days with positive surprises are characterized by a large average price drop of about 0.5%traders take short positions as inventories are larger than expected -which is partly corrected in the hours following the news release. Four hours later, the drop is half as large, i.e. 0.25%. In other words, the futures price over-reacts to the announcement. Over-reaction is the subject of intense research in behavioral finance. Recent contributions have attributed over-reactions to the general sentiment in financial markets, while earlier work argued for over-confidence, positive feedback trading or herding. 27 In our context, over-reaction may be viewed as an efficient inefficiency (Pedersen ( 2015)), as it is not clear how it could be robustly exploited for trading purposes.

Fourth, days with negative surprises do not show any particular pattern in the pre-announcement period. At the time of the news release, we then observe a positive jump -inventories are lower than expected -which effect is much smaller in size than its counterpart for positive surprises. Specifically, the average rise is about 0.15%, which is three times smaller than that when inventories larger than expected. Negative surprises seem to have then only a limited impact on oil-futures prices. Again, there is an over-reaction in the hours following the announcement, as for positive surprises, with the initial price jump being absorbed by, on average, 2:30 p.m., i.e. at the end of the open outcry session.

Overall, it is clear that, i) for positive surprises, there is a significant run-up from 8:30, ii) prices respond to surprises, meaning that news really is news and is not common knowledge, and iii) price over-reacts to surprises.

Empirical analysis

This section first presents the order-imbalance method and then the empirical results and robustness analysis with respect to alternative definitions of surprises.

Empirical Method

To see whether there is trading based on private information regarding the EIA inventory announcement, we consider trading activity in the futures market in the hours before the news release. Assuming that informed traders need immediate execution to exploit their informational advantage, 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 -1% -0.5% 0 +0.5%

time (eastern time)

Cumulative returns

No surprise

Positive Surprises

Negative Surprises

Figure 5

Cumulative returns in announcement days.

we imagine that they will trade at the best price offered by their counterparts. It is then common to measure informed trading by order imbalance OI, defined as:

OI = (B -S)/(B + S)
where B is the volume of buyer-initiated trading and S the volume of seller-initiated trading over a given period of time. Volume can either be defined as the number of trades or the dollar trading volume, producing OIN and OID respectively. When there is considerable variability in order size, it is better to work with OID so as not to overweight small trades. Our baseline analysis will use OID. 28 The presence of informed trading will be revealed in OI that are significantly higher on days of surprises. Large order imbalance is a strong indication, although not a formal proof, of trading based on private information.

Our data for the WTI futures price do not indicate whether the transaction arises from a buyer or a seller. As is common for such data, we rely on the tick rule to classify transactions as buyer or sellerinitiated. When the transaction price is above [below] the previous transaction price, we consider the transaction as being buyer [seller] -initiated. 29 One issue with our data is that they are stamped only to the second, meaning that several transactions can occur in one second. We follow [START_REF] Bernile | Can information be locked-up? Informed trading ahead of macro-news announcements[END_REF] in calculating the volume-weighted price for the second of interest, and then rely on the tick rule to classify the bulk of transactions in this second.

Main findings

Figures 6 and 7 plot the OID computed over short intervals of 2 and 5 minutes, respectively, for the periods two and a half hours before and after a typical announcement. 30 In the top panel, we plot the OID for days with announcements the value of which was in line with expert expectations.

In the middle and the bottom panel, we analagously plot the 2 and 5-minute OID for positive and negative surprises, respectively.

On days without surprises, OIDs are small over the whole period, with no particular pattern. In contrast, on days with surprises, either positive or negative, we observe large OIDs immediately after the announcement. This finding confirms our conclusion from the observation of the cumulative returns (Figure 5) that news release has a large impact on the oil-futures price when the inventory level is different from the expected value. Note that these large OIDs are particularly relevant in light of the huge trading volume (B+S) in the oil-futures market every Wednesday at 10:30 a.m. (see Figure 4). [START_REF] Admati | A theory of intraday patterns: Volume and price variability[END_REF] set out a theory explaining the clusters in trades during the trading session. This concentration of trading comes from the strategic behavior of liquidity and informed traders who have incentives to trade at the same time. The spike in trading volume at the time of announcement, either for surprises or not, is also consistent with the search for the resolution of uncertainty. In summary, the news release is an event for the oil market, and market participants trade heavily to adjust their positions at the time the inventory level is made public.

For negative surprises (the bottom panels), the OIDs are much larger than for days with no surprise, but appear largely random: we cannot identify any particular pattern either in the pre-or the post-announcement periods. Conversely, in days with positive surprises, we observe OIDs that are mostly in line with the subsequent announcement surprise (buyer-initiated orders). In particular, in the 8:30 -9:30 range, there are almost no positive OIDs, which is indicative of trading on private information. Overall, the order-imbalance pattern is very consistent with the returns patterns plotted in Figure 5. We also conclude that the order imbalance somewhat predicts the content of the news to be delivered.

During the post-announcement period, while OIDs are small in days with no surprises, we observe the opposite pattern for days with positive and negative surprises. In the former case, the OIDs appear to be mainly positive -buyer-initiated transactions -which is in line with the over-reaction suggested in Figure 5. Similarly, OIDs are mostly negative -seller-initiated transactions -when there are negative surprises, which is also consistent with over-reaction. It should be noted, however, that large OIDs are partly due to lower liquidity -small (B + S) -in the post-announcements periods, as emphasized in Figure 4.

Statistical significance

To gauge the statistical significance of the patterns in Figures 6 and7, we run the following regression:

OI t = α + β.I(SU R > 0) + γ.I(SU R < 0) + ε t
We here regress the OID over a given time interval on two indicator variables: the first for positive surprises and the second for negative surprises. We expect the OIDs to be significantly related to the indicator variables, at least in the case of positive surprises. The coefficient estimates appear in Table 3. Estimation is by ordinary least squares. 31The results clearly indicate a significant association between the dummy for positive surprises and the average OID over the 8:30 -9:30 period. Order imbalance based on number of trades considering 3σ surprises (2 minutes).

of buyer-initiated orders before the public release of inventory levels that are higher than expected.

For the extended period 8:30 -10:29, the relationship between the dummy and OID also appears, but is less significant. When we extend the period over which the OID is calculated, we lose significance. In particular, the OID during the 4-hour pre-announcement period is not significantly linked with surprises. Similarly, considering the last hour before the news release only does not lead to significant estimates for the indicator variables. As such, we conclude that order imbalance is concentrated in the hour surrounding the opening of the open outcry session.

One important feature of our results is the asymmetry between positive and negative surprises.

While we obtain statistically-significant estimates for inventories that are higher than expected, the estimates for lower than expected inventories reveal no abnormal order imbalance for any subperiod of the pre-announcement period. The question then arises as to whether there is more order imbalance in case of positive surprises. We suggest that informed traders in the oil-futures market face different situations when expecting good or bad news. In the case of good news, participants Order imbalance based on number of trades considering 3σ surprises (5 minutes). Order imbalance based on number of trades and 4σ surprises (2 minutes).

Conclusion

This paper has provided evidence of a clear run-up price effect of about 0.25% and large sellinginitiated transactions ahead of the EIA-DOE inventory release each Wednesday, when the level to be released is higher than expected. The significant order imbalance takes place around the open-

ing of the open outcry session, when increasing liquidity facilitates discreet trading. Our results, derived from high-frequency data, are robust to alternative definitions of surprises and are consistent with potentially informed trading. However, we are not able to formally conclude that private information is used by insiders, as our data does not allow us to identify the channel by which this information would enter the market.

Our analysis has a number of implications with respect to the Weekly Petroleum Status Report.

First, it raises the question of whether EIA lock-up practices are really secure. This calls into question the overall security of raised recently in numerous media (Wall Street Journal (2013[START_REF] Verstein | Insider Trading in Commodities Markets[END_REF]).

Second, our work also has regulatory implications, as we might wish for the better monitoring of oil markets around announcements to better assess the presence of insider trading. Third, the time chosen for the announcement could also be changed to limit the potential for insider trading in times of high liquidity. For instance, an announcement at 8:30 a.m. might help to moderate the activity of insiders in the oil0-futures market in the pre-announcement period.

One limit of our work, which also is a possible avenue for future research, lies in the transactions that we cannot relate to traders. The use of data with the type of traders, or possibly their identity, at the tick-data level would reveal new features. Existing work such as [START_REF] Phillips | Information and normal backwardation as determinants of trading performance: Evidence from the North Sea oil forward market[END_REF] uses trade-level data to gauge the profit for various types of traders in the North Sea oil forward market.

Using individual trade data, the authors are able to identify the types of traders that make profits, and so test the theory of insurance or information in forward trading. In the same vein, [START_REF] Ederington | Who trades futures and how: Evidence from the heating oil futures market[END_REF] use CFTC-type data on traders' positions in the heating-oil futures market to gauge the hedging and speculative behavior of various agents. Both contributions have valuable data on trades, making their analysis innovative with respect to trader behavior in oil markets. However, they do not investigate the response of prices and/or volume to news.

Other potential lines of research include the analysis of informed trading for alternative oil-related assets such as options on futures, stocks of oil companies or the FX rates of oil-exporting countries.

We have also not exploited the apparent over-reaction to news. In the vein of papers dealing with short-term contrarian strategies based on news, the economic value of these patterns could then be estimated.

Figure 2 Figure 3

 23 Figure 2Number of 3σ surprises vs. no surprise by week-of-the-year

  2006 -, through CME Globex and ClearPort, and via an open outcry session.22 The open outcry session used to cover the 9:00 a.m. -2:30 p.m. period from Monday to Friday, while the electronic market was open 6:00 p.m. -5:00 p.m. from Sunday to Friday. All WTI futures contracts are quoted in U.S. dollars per barrel, and the contract unit is 1,000 barrels with a delivery point at Cushing, Oklahoma.23

  Figure 4Volume (number of contracts) by 5-minute interval.

Figure 6

 6 Figure 6

Figure 7

 7 Figure 7

Figure 8

 8 Figure 8

Table 1

 1 Summary statistics of U.S. oil inventory actual values and analysts' forecast errors table reports sample statistics of oil inventories for the period between February 7th 2007 and October 22nd 2014."Actual values" refer to the EIA actual oil inventory data, "Median estimates" are the median forecasts from the weekly Bloomberg survey, "Forecasting errors" are the difference between the actual value and the median forecast of each weekly oil inventory, and "Abs forecasting errors" are the absolute value of this difference.

	N	Mean* Std dev.* Median*	Min*	Max*
	Actual values 402 124.97	3846.58	466.50	-11120.00 10013.00
	Median estimates 402 241.73	1740.00	750.00	-4950.00	3200.00
	Forecasting errors 402 -116.76 3255.74	94.50	-10120.00 9033.00
	Absolute forecasting errors 402 2533.28 2048.39	1990.50	11.00	10120.00
	* Thousands bbl.

The

  This finding is supported by oil-market practitioners who are doubtful about the value added of the API survey. In Bloomberg (2013), the chief market strategist at Confluence Investment Management in St. Louis says: "You can go to jail if you don't contribute to the DOE and if you contribute bad numbers they can sue you. The API asks its members to contribute. The members sometimes don't, and sometimes they give the API incomplete numbers." The article further mention that "Valero Energy Corp., the San Antonio-based biggest independent refiner in the U.S., is not a member of the API and doesn't participate in the weekly survey. Valero produced 910,000 barrels a day of distillate fuel, including heating oil and diesel, in the second quarter[2013], according to the company's earnings release. That's about 20 percent of the 4.66

	million barrel a day U.S. output reported by the EIA".
	The open outcry session ceased on July 7th 2015, which was considered to be a disappointing change by historical traders
	(Financial Times (2015)).
	Further	specifications	on	WTI	futures	contracts	can	be	found	on	the	CME	website	at:
	http://www.cmegroup.com/trading/energy/crude-oil/light-sweet-crude_contractSpecs_futures.html.

  The t-statistic is large (3.154), revealing the existence

		Days with announcement but no surprise
	10%
	0
	-10%
	8:00	9:00	10:00	11:00	12:00	13:00
			Days with positive surprise
	10%
	0
	-10%
	8:00	9:00	10:00	11:00	12:00	13:00
			Days with negative surprise
	10%
	0
	-10%
	8:00	9:00	10:00	11:00	12:00	13:00

Table 3

 3 Ordinary least squares estimates for 3σ surprises

	Event windows Intercept PosSUR NegSUR
	[-240, -120]	-0.639	0.630	0.135
		-1.412	0.469	0.105
	[-240, 0]	-0.438	-0.568	0.300
		-1.747	-0.767	0.420
	[-120, -60]	0.212	-3.529	-0.009
		0.522	-3.154	-0.008
	[-120, 0]	-0.221	-1.758	0.389
		-0.820	-2.304	0.588
	[-60, 0]	-0.628	0.433	0.717
		-1.886	0.458	0.778

Table 4

 4 Regression analysis of order-imbalance measures for 4σ surprises This could reflect the smaller number of surprises. Moreover, we fail to identify any significant relationship between OID and negative surprises, possibly due to the smaller number (22) of negative surprises.

	Event windows Intercept PosSUR NegSUR
	[-240, -120]	-0.592	1.519	-0.694
		-1.413	0.951	-0.298
	[-240, 0]	-0.446	-0.116	-0.278
		-1.891	-0.126	-0.244
	[-120, -60]	0.060	-3.633	-0.372
		0.158	-2.441	-0.272
	[-120, 0]	-0.270	-1.956	-0.118
		-1.063	-2.103	-0.152
	[-60, 0]	-0.500	-0.350	-0.415
		-1.600	-0.278	-0.376

Well-known references in this area include[START_REF] Andersen | Micro effects of macro announcements: Real-time price discovery in foreign exchange[END_REF]), Vega (2006) or[START_REF] Lucca | The pre-FOMC announcement drift[END_REF].

Earnings are, of course, one of the most important items of news for individual stock prices, and have been the subject of intense research in financial economics in recent years.

See Bhattacharya and Daouk (2002) for a survey of insider-trading law enactment and their different enforcement across the world. See also[START_REF] Bhattacharya | Insider trading controversies: A literature review[END_REF] for a general discussion of insider trading in equity market.

This Financial Reform Law is over 2,000 pages long and represents a sweeping attempt to prevent future financial crises.

This refers to the film "Trading Places" where two commodity brokers, Eddie Murphy and Dan Aykroyd, make a profit on the frozen orange juice market using a stolen orange crop report from the U.S. Department of Agriculture (USDA).

Further information about the full content of the WPSR can be found on the EIA website at: http://www.eia.gov/petroleum/supply/weekly/pdf/wpsrall.pdf.

850,000 WTI futures and options of all maturities are traded daily. The total open interest is over 3 billions barrels.

For days with no announcement, the volume pattern is similar to that found in the literature on market microstructure, i.e.higher trading volume at the start and end of the trading session and a U-shaped relationship between the start and end, consistent with[START_REF] Admati | A theory of intraday patterns: Volume and price variability[END_REF].

Note, in addition, that our data include 43 positive surprises over a seven and a half year period, while the sample in[START_REF] Bernile | Can information be locked-up? Informed trading ahead of macro-news announcements[END_REF] includes only 25 surprises over the period September 1997 -June 2013, i.e. about 15 years. This highlights the potential profit for insider trading around EIA news in the oil-futures market.

We refer the interested reader to the survey in[START_REF] Barberis | A survey of behavioral finance[END_REF] for an exposition of the existing literature on, among other phenomena, over-reaction. This interesting issue is beyond the scope of the present paper.

We have replicated the full empirical analysis using OIN as the measure of informed trading. The results, similar to those using OID, are not reported here but are available on the author's website (https://sites.google.com/site/benoitsevi/).

[START_REF] Bernile | Can information be locked-up? Informed trading ahead of macro-news announcements[END_REF] explain the use of the tick rule in the context of the literature on empirical market microstructure. We refer the interested reader to their footnote 11 for further explanations.

We have also calculated OIDs for one and ten-minute intervals and obtain similar patterns. Extending the analysis period also produces similar results.

In preliminary work, we ran regressions with a dummy variable for days with an announcement, as in[START_REF] Bernile | Can information be locked-up? Informed trading ahead of macro-news announcements[END_REF].This did not attract a significant estimated coefficient. We thus only report results for the surprise dummies. The extended results, which are qualitatively similar to those presented here, are available upon request.

We wish to thank Philippe Bernard from Université Paris Dauphine for helping us with the data from Bloomberg. We also thank Rob Merriam and Mark Mayo from the Energy Information Administration (EIA) at the Department of Energy thank Marketa Halova Wolfe for useful comments about Bloomberg's survey of oil-market participants.

speculating on price drops and owning the commodity at the same time are naturally hedged. Conversely, when bad news is anticipated and the oil-futures prices is expected to increase, speculation is more risky as hedging is not feasible.

Robustness analysis

We consider a number of alternative definitions of surprises. We also considered surprises defined as in [START_REF] Bernile | Can information be locked-up? Informed trading ahead of macro-news announcements[END_REF] and [START_REF] Ye | The informational content of inventory announcements: Intraday evidence from crude oil futures market[END_REF]. These definitions are more sophisticated, as they involve preliminary regressions, but their use does not change our results. These methods end up identifying the same surprises as our simple definition used throughout the paper. Another surprise definition is that in [START_REF] Andersen | Micro effects of macro announcements: Real-time price discovery in foreign exchange[END_REF], which is (Act t -F or t )/ σ t where Act t is the released value, F or t the predicted value and σ t the sample standard deviation of the surprise component Act t -F or t over the past 100 weeks. We have also experimented with surprises defined as realized values outside the range of forecasts. The results are again very similar to those in our baseline analysis.

To save space, we here only present the results when the surprise is defined as actual values that are over four standard deviations from the median forecast: these are coined 4σ surprises. The number of surprises drops by about a half, as can be seen in the bottom panel of Table 2. Again, we do not identify any specific pattern over the years. The number of surprises by year is between 4 and 7. We end up with a total of 23 positive surprises and 22 negative surprises over the period of interest.

The order imbalance for 2-minute intervals is plotted in Figure 8, and is quite similar to that for 3σ surprises. There is order imbalance in the selling direction for positive surprises in the 8:30 -9:30 period. We also note a cluster of notable buyer-initiated orders around 9:00 in days with negative surprises.

The estimates for the regression using 4σ surprises appear in Table 4. The results are qualitatively similar to those in Table 3, where surprises are defined less strictly. The t-statitics are, however,