Insaf Ajili

Malik Mallem

Jean-Yves Didier

An Efficient Motion Recognition System Based on LMA Technique and a Discrete Hidden Markov Model

Keywords: Human Motion Recognition, Motion representation, Laban Movement Analysis, Discrete Hidden Markov Model

Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a novel descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures (Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

I. INTRODUCTION

Human motion recognition is an active area of research due to its importance in many applications: video surveillance, indexing videos, interaction Human-machine, security, and health-care. The purpose of a human motion recognition system is to recognize simple actions of everyday life such as running, knocking, eating, walking, etc.) from videos. The problem of human motions recognition has attracted the attention of several researchers and the advantages and limitations of the different proposed approaches have been discussed over the last years. Two crucial aspects of motion recognition are to extract relevant features by representing the contents to be classified by a descriptor vector and to develop a robust learning algorithm in order to associate with this representation a label. Many descriptors have been chosen in the computer I.Ajili, M. Mallem and J. Didier are with IBISC, Univ Evry, Université Paris-Saclay, 91025, Evry, France. vision literature. First methods based on interest points have been proposed to describe human movements by Laptev and Lindberg [START_REF] Laptev | Learning realistic human actions from movies[END_REF]. They have used the Harris 3D point-of-interest detector, which is an extension of the Harris detector, adding to it the temporal dimension. Dollar et al. [START_REF] Dollar | Behavior recognition via sparse spatio-temporal features[END_REF] have also proposed a similar detection algorithm, the cuboid detector, based on interests points calculated from Gabor's filter responses in the space and time domain. Other approaches have illustrated the relevance of tracking point trajectories for recognizing actions in videos. For instance, Messing et al. [START_REF] Messing | Activity recognition using the velocity histories of tracked keypoints[END_REF] have extracted features trajectories by tracking Harris3D interest points with the help of the KLT tracker. Also Matikainen et al. [START_REF] Matikainen | Trajectons: Action recognition through the motion analysis of tracked features[END_REF] have extracted trajectories of tracked feature points in a bag of words paradigm for action recognition. Wang et al. [START_REF] Wang | Dense trajectories and motion boundary descriptors for action recognition[END_REF], have extracted features aligned with the trajectories to characterize appearance and motion. To reduce the influence of camera motion on action recognition, they introduced a descriptor based on motion boundary histograms (MBH) which rely on differential optical flow. When trying to identify human motions, it is sometimes wise to know where the actor of the action is in order to isolate him from the rest of the scene. This allows focussing on his movements, regardless of what happened in the background. To do this, two paths have been widely studied, the first one was the analysis of the shape of the character, through his silhouette, here we can cite the work of Shao and Chen [START_REF] Shao | Histogram of body poses and spectral regression discriminant analysis for human action categorization[END_REF] who have employed body poses sampled from silhouettes that were fed into a bagof-words model. Also, Ahmad and Lee [START_REF] Ahmad | Variable silhouette energy image representations for recognizing human actions[END_REF] have proposed a spatiotemporal silhouette representation, called Silhouette Energy Image (SEI) to differentiate the properties of form and motion for the human action recognition. The second path was the analysis of the movement of the actor through the identification of his limbs (hands, head, legs, etc.). Zanfir et al. [START_REF] Zanfir | The moving pose: An efficient 3d kinematics descriptor for low-latency action recognition and detection[END_REF] have proposed a moving pose descriptor defined by both pose information and differential information (velocity and acceleration). Hussein et al. [START_REF] Hussein | Human action recognition using a temporal hierarchy of covariance descriptors on 3d joint locations[END_REF] have introduced the covariance matrix of skeleton joint locations over time as a descriptor vector. In order to code the temporal dependence of the joints positions, multiple covariance matrices have been deployed over sub-sequences in a hierarchical way. Yang et al. [START_REF] Yang | Effective 3d action recognition using eigenjoints[END_REF] have introduced both spatial and temporal aspects. Their descriptor vector included three pieces of information, the static pose posture information (fcc), the temporal movement of a pose defined by the difference between the current and the previous pose (fcp) and the offset from an initial pose (fci).

In our work, we tried to propose more suitable features and advanced learning algorithm to improve human motion recognition performance. Our descriptor based on a Laban Movement Analysis method (LMA) introduced by Rudolf Laban (1879 to 1958) to analyze, describe, visualize and annotate all varieties of human motion using a specific notation. LMA is a descriptive language widely used in the field of dance, physical therapy, athletics, and behavioral science. It is generally used to analyze the movement of dancers and athletes. It captures both quantitative and qualitative aspects of the movement by encoding Laban components. To model human motion data we used DHMM method for motions recognition. Classification step is based on Forward algorithm [START_REF] Rabiner | An introduction to hidden Markov models[END_REF] where each motion is presented with two DHMMs to encode motion sequence in the forward and backward directions. By applying Forward algorithm in the two cases we avoid the conflict that can happen between some gestures which share a same part of the motion. For example, extend your arm straight forward and knocking the door are two actions having the same motion in the first frames, which can lead to a misclassification between them. So, the idea here is to classify both motions with taking into consideration the two motion sequence directions which can help to make a distinction between them.

The remainder of the paper is organized as follows. In Section 2, we describe our proposed approach starting with preprocessing data step, feature extraction and finally gesture recognition step. Experimental results are reported in section 3. We first evaluate our descriptor vector with a public dataset MSRC-12 and with our dataset dedicated to control gestures. We compare between recognition results obtained by the basic DHMM method and our proposed DHMM method. Finally, conclusion and future work are presented in Section 4.

II. HUMAN MOTION RECOGNITION SYSTEM

In general, a human motion recognition system is composed of three important steps: preprocessing data, feature extraction, and motion recognition (Fig. 1). In the first step, we introduce an invariant approach to make our system independent of the initial position and orientation of the user. In the following step, to represent human motion, we used three LMA components, Body, Space, and Shape. We didn't use Effort component because it describes the qualitative use of energy and the inner attitude. Effort qualities depend on the rhythm, weight and the intention of the motion and are often used to describe emotions. However, our application consists in recognizing human gestures regardless their rhythm. So, if we make the same gesture at different speeds our system should give the same result. In the recognition step, we used DHMM model which accepts discrete values as input. A discretization approach was implemented following a quantization algorithm (kmeans) in order to generate a set of discrete values which will be implemented into Baum Welch algorithm for training data. In the classification step, we presented each gesture with two DHMM models to process the gesture sequence in two opposite directions and apply the forward algorithm to conclude the action label for a testing gesture.

A. Data Collection And Preprocessing

Prior to the extraction of features, a preprocessing data step is applied to each gesture sequence captured by kinect sensor. So, first we define a local skeleton coordinate system (X , Y , Z ) which origin is the hip center joint. X -axis is the vector starting from the right hip center and going to the left hip center. Y -axis is the vector connecting between the midpoint of hips (J c ) and the torso joint (P t ). And finally Z -axis is orthogonal to both vectors.

i = J lhi -J rhi J lhi -J rhi ; j = J s -J c J s -J c ; k = i ∧ j (1)
Let [J j ] (X,Y,Z) be the 3D position of joint j presented in the camera coordinate system (X, Y, Z). We translate the skeleton coordinate system to the center of kinect:

[J j ] X = [J j ] X -c x ; [J j ] Y = [J j ] Y -c y ; [J j ] Z = [J j ] Z -c z (2)
Where J c (c x , c y , c z ) is the 3D position of the hip center joint in the kinect coordinate system. After we apply a rotation to align both coordinate systems, we have:

[J j ] (X,Y,Z) = R (X,Y,Z)←(X ,Y ,Z ) [J j ] (X ,Y ,Z ) (3) = [r 1 r 2 r 3 ][J j ] (X ,Y ,Z ) (4)
where r 1 , r 2 and r 3 are the rotation vectors around the X -Axis, Y -Axis and Z -Axis respectively.

By applying (4), we have:

r 1 = [J lhi ] (X,Y,Z) a ; r 2 = [Js] (X,Y,Z) c
and r 3 = r 1 ∧ r 2 where a is the distance between C and J lhi , and J c is the distance between J c and J s . Since r 1 , r 2 and r 3 are orthogonal unit vectors, hence R T = R -1 . Then:

[J j ] (X ,Y ,Z ) = R T (X,Y,Z)←(X ,Y ,Z ) [J j ] (X,Y,Z) (5)

B. Feature Extraction

Feature extraction can be defined as the extraction of significant features from raw data, which maximizes the difference between class patterns while enhancing the variability between class patterns. Our descriptor vector is derived from LMA approach which can describe and interpret all varieties of human movements. For human motion representation we quantify three LMA components: Body component, expresses which body parts are moving and how their movement are related to each other. It also addresses issues concerning locomotion and kinematics by describing structural and physical characteristics of the human body. Body organization is related to the connection between body parts. In the upper body part, we describe the extension of elbows (θ l 1 , θ r 1 ,) and shoulders (θ l 2 , θ r 2 ). And we describe outstretched arms by computing the distance between two hands (d Hs ). To know more about hands pathway, we add three others features, the distances between the shoulder center and left (d shc,lh ) and right hand (d shc,rh ), and the angle between two hands with respect to shoulder center joint(θ Hs ). In the lower part we describe legs extension (θ l 3 , θ r 3 )) and legs spread (θ Ls ). Space component defines where in space the motion is happening, the directions and spatial patterns. We define the torso direction by computing the normal vector N of the triangle formed by left hip (hi l ), right hip (hi r ) and neck (n) joints.

- → N = --→ nhi l ∧ --→ nhi r || --→ nhi l ∧ --→ nhi r || (6)
Shape component describes the way the body changes shape during movement. It focuses on two main qualities, "What forms does the body make"?, "Is the shape changing in a self-to-self relationship or in relation to a goal in space"? ". Shape category is composed of three subcategories: Shape Flow, Carving, and Directional Movement.

Shape flow represents the relationship of the body to itself. It is related to the shape deformation, expanding or condensing, during movement. We compute the volume of the smallest convex envelope of the human body based on Quickhull algorithm [START_REF] Barber | The quickhull algorithm for convex hulls[END_REF]. Directional movement, represents the pathway through space of the movement, either curving or straight. We quantified how curved are the trajectories made by the upper extremities (head and hands) by computing the gradual angular change φ occurring by each joint between two successive frames.

φ Jt = arccos(

----→ Jt-1Jt ----→ Jt-1Jt • ----→ JtJt+1 ----→ JtJt+1 ) (7)
where J t/t+1 is the position of joints (head and hands) at frame t and t + 1, respectively. This equation describes the local curvature of the upper body parts'pathway. So in direct movement with straight-line trajectories, local curvature feature will be closest to 0. But in curved trajectories, it will be higher.

Carving describes the qualitative changes in the shape according to three planes horizontal, frontal, and sagittal, and relating them to bipolar descriptors like spreading and enclosing, rising and sinking, and retreating and advancing, respectively. We quantify Carving factor by computing the projected distances between head, upper and lower extremity joints (hands, elbows, knees, and feet) relating to spine joint pose at the initial frame. These projections provide the frontal, sagittal and horizontal displacements of the head, arms, and legs.

d = d (p je -p se ) 2 (8)
where j represents each joint considered at each frame, s is the spine joint at the initial frame and e belongs to one of the following sets {x, y}, {y, z} and {z, x} for each considered projection.

C. Recognition phase

The problem of human motions recognition can be reduced to a problem of supervised classification. In order to make a decision on a given motion, the system performs two steps: training and testing. The purpose of the training phase is to build a set of rules that will be used for the recognition of future actions. Indeed, based on the labeled data (the truth ground), the system is capable of building decision rules to be able to distinguish between the different categories of human motions. By applying these decision rules to a given action, the system is able to predict its class. In this paper, we use the Hidden Markov Model for both training and classifying motions.

1) Discrete Hidden Markov Model: Hidden Markov Models [START_REF] Rabiner | An introduction to hidden Markov models[END_REF] is one of the most well-known methods in machine learning. An HMM is a statistical model for time series, used to represent the evolution of observable sequences (O) that depend on unobserved, or discrete state variables (S). We modeled each gesture with a left-right HMM. In such model, only transitions from one state to itself or to a unique successor are allowed.

A set of trained HMMs for the C classes of gestures can be represented as σ {1,...,C} = {σ 1 , σ 2 , . . . , σ C }.

The HMMs we use are discrete HMMs and discrete HMMs. They accept only discrete values as inputs. Thus, given our descriptor vector, before implementing it into an HMM, we use discretization and quantization algorithms during training and classification steps. In discretization approach, we implemented a C++ algorithm presented in Algorithm 1 which consists in sampling gesture sequences with different sizes into a fixed-size T . A gesture sequence s in = {f 1 , f 2 , . . . , f N } is defined as a N ×d matrix composed of N feature vectors with d features, f k is a feature vector recording at frame k with size d (d=number of features). The output of our discretization algorithm is a T × d matrix presented a gesture sequence composed of T feature vectors s out = {h 1 , h 2 , . . . , h T }.

Algorithm 1: Discretization algorithm.

Input : After the discretization step, we apply k-means algorithm [START_REF] Macqueen | Some methods for classification and analysis of multivariate observations[END_REF] to cluster the feature vectors of all gesture sequences into K clusters {c 1 , c 2 , . . . , c K } in which each feature vector belongs to the closest cluster, so as to satisfy the condition expressed by:

s in = {f 1 , f 2 , . . . , f N }, T Output: s out = {h 1 , h 2 , . . . , h T } 1 g 1 = f 1 2 j ← 2 3 k ← 1 4 for i ← 2 to N do 5 Compute D = d l=1 (f i,l -f k,l ) 2 D is the distance between two f eature vectors f i and f k . if D ≥ then 6 g j = f i 7 j ← j + 1 8 k = i
argmin c K j=1 hi∈cj h i -µ j 2 (9)
where µ j is the mean of the elements in the cluster c j . At the end of the quantization algorithm, a gesture sequence will be presented as an observation sequence O = {o 1 , o 2 , . . . , o T }, where o i is a discrete symbol ∈ {c 1 , c 2 , . . . , c k }. So each symbol o i corresponds to the cluster of the feature vector f i , and the output sequence length is T , which acts as the discrete observation sequence to be the input observation sequences to learn the HMM model and use this model to predict for the unknown sequence.

The parameters of the model HMM can be represented in the compact way σ = (π, A, B), where π is the initial probability distribution over states, A is the transition probability matrix, and B is the matrix that represents the emission probability of a symbol observed from a specific state.

For the training step, we use the Baum-Welch algorithm to find optimal parameters to the HMM given an initial model σ i = (π i , A i , B i ) and the observations sequences {O 1 , O 2 , . . . , O s } corresponding to the learning gesture sequences.

σ * = argmax σ ( s i=1 logP (O i |σ)) (10)
For classification step, we use the Forward algorithm to classify a sequence test O t = {o 1 , o 2 , . . . , o T }. The class label is assigned via Maximum Likelihood after evaluating the sequence in every HMM.

C = argmax allσ (log(P (O t |σ)) (11)
2) Modified DHMM: In the classification step, sometimes we have very similar motions, in such case our recognition algorithm can make a misclassification gesture. An example is provided in Fig. 3 which shows two similar gestures (G1 and G2). Gesture 2 shares a part with Gesture 1 in the first three initial frames. In such case, initial state probabilities and transition probability matrices of the two models are very close which can lead to an error classification.

The idea here is to process the gesture sequence in two directions the forward (from the first frame to the last frame) and backward (from the last frame to the first frame). We define two models for each gesture σ d G and σ i G are the DHMM models when considering the gesture sequence (G) in the forward direction and in the backward direction, respectively. If we take a test sequence (O d t ) of the second gesture (G2), we have:

P (O d t |σ d G1 ) ≈ P (O d t |σ d G2 ) (12)
Now if we consider the observation sequence in the backward direction (O i t ) we have:

P (O d t |σ d G2 ) ≈ P (O i t |σ i G2 ) (13) P (O i t |σ i G2 ) > P (O i t |σ i G1 ) (14)
According to (12) [START_REF] Truong | Dynamic gesture recognition with laban movement analysis and hidden markov models[END_REF], we obtain the following Equation:

min(P (O d t |σ d G2 ), P (O i t |σ i G2 )) > min(P (O d t |σ d G1 ), P (O i t |σ i G1 )) (15)
According to [START_REF] Wang | Dense trajectories and motion boundary descriptors for action recognition[END_REF], we can declare the class label of the test sequence (O t ) as:

C = argmax allσ min(log(P (O d t |σ d G )), log(P (O i t |σ i G ))) (16)

III. EXPERIMENTAL RESULTS

We evaluated the robustness of our descriptor and our classification method for action recognition. We performed this evaluation on two datasets: The first one is a public dataset (MSRC-12) acquired using a Kinect sensor and the second dataset is our dataset dedicated to control gestures built under Robot Operating System (ROS). We applied our descriptor vector and modified DHMM method for action recognition step. Details of the experiments are presented in the following subsections.

A. MSRC-12 Dataset [4]

To evaluate our approach, we tested our method on a relatively large dataset captured by a Kinect sensor, the MSRC-12. The dataset is composed of 12 gestures performed by 30 subjects. Each subject repeats the same gesture several times. In total, there are 6244 gesture instances. The motion files contain 3D coordinates of 20 joints captured at a sample rate of 30Hz with an accuracy of about 10 centimeters in joint positions.

The dataset is divided into two groups: iconic (hide, shoot pistol, throw object, change weapon, kick, put goggles) and metaphoric (raise volume, navigate to next menu, wind up music, take a bow, protest music, low down song) gestures. We converted the raw data into a descriptor vector based on the three LMA components. After, we discretized gesture sequences into a fixed length by applying discretization method presented in Algorithm 1 with T = 70 frames. A grid search on DHMM parameters (number of states S, and number of

Methods

Iconic Metaphoric Lehrmann et al. [START_REF] Lehrmann | Efficient nonlinear markov models for human motion[END_REF] 90.90 -Song et al. [START_REF] Song | Distribution-sensitive learning for imbalanced datasets[END_REF] 79,77 81 Truong et al. [START_REF] Truong | Dynamic gesture recognition with laban movement analysis and hidden markov models[END_REF] 88. In order to compare our approach with state of the art methods, we used the more challenging validation method, the cross-subject test where one-half of the subjects are used for training and the remaining for testing.

Fig. 4 demonstrates that our method achieves an average accuracy of 94.03% and 80.48% on iconic and metaphoric gestures respectively when applying basic DHMM method. With our modified DHMM we improved the recognition results with an average accuracy of 96.33% for iconic gestures and 90.66% for metaphoric gestures.

The results in Table I show the comparison between our result and state of art results. For a faithful comparison, we take the result of Truong et al. [START_REF] Truong | Dynamic gesture recognition with laban movement analysis and hidden markov models[END_REF] where they used the same learning method DHMM. Our method outperforms their method by 7.73% on iconic gestures and 15.46% on metaphoric gestures. In general, we can say that our method performs better than the state-of-the-art result.

B. Control gestures dataset

After evaluating our system with a public dataset, we built our dataset composed of ten control gestures (move, introduce, turn left, stop, turn right, increase velocity, decrease velocity, waving, dance, introduce yourself) as shown in Fig. 9. Twenty subjects (10 men and 10 women) from the University of Evry Val d'Essonne, ranged in age from 27 to 45 years old (M=28.5 years, SD=5.5) took part in this study. Each subject is asked to make gesture ten times. Our dataset has in total 2000 sequences (20 subjects× 10 gestures× 10 times). Our system was implemented under ROS, a Robot Operating System which consists in running a great number of executables to exchange data synchronously (via topics) or asynchronously (via services). For data acquisition, the OpenNI driver provided a high-level skeleton tracking module. This module requires initial calibration to record the 3D position of skeleton joints at 640 × 480 resolution at 30 fps.

For each gesture, we computed the descriptor vectors from raw data. After, we sampled descriptor vectors with 30 frames. Two examples of the discretization of two extracted features from our descriptor vector are shown in Fig. 6 and Fig. 7. The first feature (d Hs ) is the distance between hands in "stop" gesture. As we can see in Fig. 6, when performing stop gesture this feature tends to zero at the end of the gesture. When applying our discretization algorithm with T= 30 frames, we convert the gesture sequence from a vector with 84 frames to a vector with 30 frames. For the "waving" gesture we take as feature example the angle between left hand and left shoulder. As shown in Fig. 7, at initial frame the angle θ l 2 ≈ 180 deg after it drops to ≈ 20 deg and finally it returns to its initial value (≈ 180 deg). We sampled the feature sequence of θ l 2 in "waving" gesture from 118 frames to 30 frames. We set the number of clusters in K-means algorithm to 20 and the number of hidden states to 5. As shown in Fig. 8, our results were very satisfying with an average accuracy result of 95% when applying simple DHMM and a significant result of 96.2% with our modified DHMM.

IV. CONCLUSION

In this paper we presented an effective approach for human motion recognition based on specific feature vectors inspired from LMA technique. A series of steps have been implemented starting with preprocessing data step based on view-invariant human motion, folowing by a suitable feature extraction method, ending with a robust recognition method based on Discrete Hidden Markov Model in order to improve the recognition accuracy of our approach. Experimental results on MSRC-12 dataset show that our method proves to be superior to some state of the art methods for skeleton-based recognition. A perfect classification performance was achieved in our dataset composed of ten control gestures. The future work will focus on enhancing our dataset by introducing expressive gestures, means gestures performed with different emotions. The main purpose of this idea is to perform expressive communicative gestures for a Human-Robot interaction.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Human motion recognition steps.

11

 11 Compute the average distance: D = N T g is the matrix of N f eature vectors obtained af ter removing noise. 12 for i ← 0 to T -1 do 13 h i+1 = g 1+i * D . 14 end

Fig. 3 .

 3 Fig. 3. Processing gestures sequences in two opposite directions for two similar gestures.

  ranging from 5 to 40 has been done. Best results are achieved with S = 20 states and O = 40 symbols.

Fig. 4 .

 4 Fig. 4. Recognition rates results of MSRC-12 iconic gestures when applying basic DHMM and modified DHMM.

Fig. 5 .

 5 Fig. 5. Recognition rates results of MSRC-12 metaphoric gestures when applying basic DHMM and modified DHMM.

Fig. 6 .

 6 Fig. 6. Discretization of d Hs feature in "Stop" gesture with T = 30 frames.

Fig. 7 .

 7 Fig. 7. Discretization of θ l 1 feature in "Waving" gesture with T = 30 frames.

Fig. 8 .

 8 Fig. 8. Recognition rates results of our dataset when applying basic DHMM and modified DHMM.

Fig. 9 .

 9 Fig. 9. Control gestures dataset.

TABLE I COMPARISONS

 I WITH STATE-OF-THE-ART APPROACHES ON THE MSRC-12

DATASET.


The measurement of financial intermediation in Japan

Keywords: JEL Classification: G10, G21, G32, G38 Disintermediation, financial system, intermediaries, capital markets Classification JEL : G10, G21, G32, G38 Désintermédiation, systèmes financiers, intermédiaires, marchés de capitaux

In this paper, we compute financial intermediation ratios for Japan on a book value basis. According to our results, the intermediation ratio has remained quite stable, at around 85%. However, this stability is the result of two opposing trends: a decrease in credits and an increase in financial securities owned by financial (mostly, non banking) institutions. These two opposing trends would not have appeared if we had used traditional indicators computed as a fraction on GDP, or that build on a narrow definition of intermediation or use market value data. Fundamentally, our results provide evidence for a very close relation between intermediate financings and market financings and tend to reject the hypothesis of the Japanese financial system's convergence toward a capital market-based system.

The measurement of financial intermediation in Japan 1. Introduction

The Japanese financial system entered, at the beginning of the 1990s, a period of major transformations. As in many countries, deregulation was a driving force of this process. For [START_REF] Hoshi | The Japanese Banking Crisis: Where did it come from and how will it end?[END_REF], by the end of this process, the Japanese financial system will converge toward a capital market-based system that implies a decline of intermediation. Such an analysis is not specific to Japan, but is often applied to many other countries (see, for instance, Allen and [START_REF] Santomero | What do financial intermediaries do?[END_REF][START_REF] Rajan | Banks and Markets: The changing Character of European Finance[END_REF]Zingales, 2003a). Three points are usually considered in this traditional approach.

The first one deals with the incidence of financial mutation on the relative importance of financial intermediaries compared to capital markets. A current view is that financial development goes against banks. Capital markets compete with banks on both sides of the bank's balance sheet. On the assets side, the increase in claims undermines the credit. On the liability side, the development of collective saving (which occurred relatively late in the case of Japan) increases the cost of resources collected by banks. As a consequence, traditional banking firms would decline and there would be a necessary redeployment towards other activities such as engineering, risk management, etc. [START_REF] Boot | Can relationship banking survive competition?[END_REF]. In the specific case of Japan, the decline of traditional banking would also go with a weakening of the main banking relationship.

The second point relates to the global evolution of financial systems. Nowadays, the majority opinion puts forward the idea of a standardization of financial systems toward a capital marketbased system, in opposition to a bank-based system. Interestingly, the only empirical study specifically dedicated to this phenomenon [START_REF] Schmidt | Disintermediation and the Role of Banks in Europe: An International Comparison[END_REF], finds neither a global trend toward disintermediation nor a convergence toward capital market-based financial systems in the major European economies (France, Germany or the United Kingdom) at a general level.

The third question concerns the relative merits of bank-based versus capital market-based systems (for a comprehensive discussion on comparative financial systems, see Allen and [START_REF] Gale | Comparing Financial Systems[END_REF], 2001[START_REF] Levine | Bank-based or Market-based Financial Systems: Which Is Better[END_REF]. For a long time, the literature has seemed to conclude in favor of capital market-based systems, at least for developed countries [START_REF] Boyd | The evolution of debt and equity markets in economic development[END_REF]. However, several recent studies using cross-country comparisons challenge this idea: although global financial development is a significant determinant of economic growth, there is no support for either the bank-based or market-based view (see Levine, 2005).

One major problem with this traditional approach is that financial structure is always challenging to quantify (see, for a discussion, Beck, Demirguc-Kunt and [START_REF] Th | The financial structure database, in "Financial Structure and Economic Growth: A Cross-Country Comparison of Banks, Markets, and Development[END_REF]. Consider, for instance, the Japanese case. While Japan is still referred to as an archetype of a bank-based system, Tokyo is one of the leading financial centers in the world; in 2003, Tokyo was ranked first for the market capitalization of newly listed domestic shares. Moreover, the Japanese corporate bond market is actually small, but the Japanese Government Bond (JGB) market is the largest in the world. At first, one can see the evidence of the convergence of the Japanese financial system toward a capital market-based system. But it seems more interesting to see the evidence of a close connection between banks and capital markets in the financial system.

Beyond the empirical problem, a more fundamental issue challenges the ground of the traditional approach. The traditional approach considers markets and intermediaries as two substitutable and opposite modalities. But one should not ignore the important interactions between markets and intermediaries: the services provided by each may overlap, and it is very likely that there is crossfertilization between markets and intermediaries. In accordance with Levine (2005), we suggest that the debate should not focus on bank-based versus market-based systems. By nature, the traditional approach cannot cover the variety of financial systems. Moreover, this dichotomous vision of financial systems is gradually being replaced by theoretical analyses that underline the complementary quality of financial services. For instance, Bodie and [START_REF] Merton | A Conceptual Framework for Analyzing the Financial Environment[END_REF]Merton ( , 2004) ) are in favor of a functional approach instead of an institutional one; in this latter case, banks and capital markets are not opposed, but assume largely identical functions (financing, portfolio management, risk management, liquidity insurance) although in a different way. Moreover, numerous studies challenge the convergence hypothesis by highlighting the role of historical or institutional characteristics like the legal system, the political context, the cultural and religious legacies, the geographical endowments or the social capital that may shape national financial systems (see, again, Levine 2005). In any case, these conflicting analyses highlight the need for better empirical measures of financial structure.

In this paper, we compute financial intermediation ratios for Japan . At the methodological level, usually, financial intermediation ratios are mostly achieved through a flow or a stock basis. However, none of these is fully suitable: flows are too erratic for long-term studies, while stock valuations amplify financial cycles. Therefore, we use stock series, but book value instead of market value. That is particularly significant in the case of Japan, whose stock exchange market was affected these last decades by episodes of very strong volatility and bull and bear markets.

Our results suggest that the distribution of external financing between banks and the market has evolved less than the nature of the banks' participation in the financing of the Japanese economy. The financial intermediation ratio has remained stable overall between 1970 and 2003, because the relative share of the investments in claims carried out by banks and other financial intermediaries has progressed and has compensated for the reduction in the share of credit. On the other hand, the nature of the intermediate financings has changed: they are, for a growing portion of them, financings by claims. That means that market financings, for part of them, are "intermediate". That makes increasingly fuzzy the traditional distinction between intermediate financings and market financings. In this respect, our study offers new stylized facts concerning the Japanese financial system: the rise of the capital markets, which started at the beginning of the 1980's, did not involve a "disintermediation" and, symmetrically, the decline in the stock exchange, at the beginning of the 1990s and at the beginning of the 2000s, did not support a "reintermediation" in Japan.

But the scope of our study goes beyond the case of Japan. Our results confirm those obtained for Europe [START_REF] Capelle-Blancard | Le financement des agents non financiers en Europe : le rôle des intermédiaires financiers demeure prépondérant[END_REF] and support a more general assessment concerning the evolution of the financial systems. Because of the close connection between market financings and intermediate financings, the theoretical cleavage between the two is less and less suitable. To characterize the structures of financing and their evolution in time, the relevant typology should be centered on intermediate financings between credit and claims investment by financial intermediaries.

The paper is structured as follows: section 2 presents the data and defines the intermediation scope. Section 3 provides results. Section 4 concludes.

Methodology

Data

Financial national accounts of Japan (Flow of Funds Accounts) are downloaded from the Bank of Japan website. We use two databases: SNA 68 data base is available for 1970-1999 (quarterly data), and SNA 93 data base ranges the period 1990-2003 (annual data until 1998, and quarterly data since). In spite of concordance problems that do appear, these two databases enable one to study the evolution of the financial system over a longer period. In Table 1, we present the financial sectors, the financial operations considered in this study, and the corresponding items in the SNA68 and SNA93 accounting system.

Intermediation ratios

A financial intermediation ratio measures the share of intermediate financings in the total of the external financings granted to the domestic non-financial sector (DNF). The denominator of the ratio (total of the external financings) is obtained by adding banking credit and claims issued by the DNF. The numerator of the ratio (intermediate financings) has two components: the first one we call "credit intermediation", and it is the total of credits granted by banks; the second one we call "market intermediation", and it is the sum of claims issued by DNF and submitted by all FI (Banks, Insurances & Pension Funds, and Others Public or Private Financial Intermediaries). 1 The sum of these two components forms what we call total intermediation. Three intermediation ratios are computed: the credit intermediation ratio (or strict ratio), the market intermediation ratio, and the ratio of total intermediation (or large ratio as the sum of the two preceding ones). Each ratio can be expressed for the DNF sector, or for the two main institutional non-financial sectors: nonfinancial corporations (NFC) and general government (GOV). The market intermediation ratio can also be broken down by type of FI active in Japan, to measure the distribution of market share between "banks" and "non-bank FI".

Market value versus book value

In a general way, market value data (like market capitalization, for example) are not appropriate to evaluate the level of financings. Such data integrate particularly significant price effects which completely hide the evolution of the financings itself (quantities effects). 2 It was obvious in the case of Japan at the end of the 1980s and the 1990s (cf. Figure 1). 2 000 000 3 000 000 4 000 000 5 000 000 6 000 000 7 000 000 1 9 7 0 1 9 7 2 1 9 7 4 1 9 7 6 1 9 7 8 1 9 8 0 1 9 8 2 1 9 8 4 1 9 8 6 1 9 8 8 1 9 9 0 1 9 9 2 1 9 9 4 1 9 9 6 1 9 9 8 2 0 0 0 2 0 0 2 0 500 1 000 1 500 2 000 2 500

3 000 SNA68 Market value SNA68 Book value SNA93 Market value SNA93 Book value Topix Notes: Outstanding amounts of stocks on a market value and a book value basis (source: Flow of Funds Account -Bank of Japan, see Table 2) on the left scale and Topix Index (source: Datastream) on the right scale.

those issued by GOV and α% of those issued by Overseas. 2 Rajan and Zingales (2003b) also recognized that equity market capitalization "captures the amount of equity listed, not the amount of equity raised. Thus, the presence of few companies that have greatly appreciated in value can give the impression of a big equity market even when the amount of funds raised in the market is tiny. On the positive side, however, this measure is less cyclical than the previous one, and thus is better for making comparisons across countries and across time periods."

Stock exchange valuation can lead to significant bias:

-A rise in securities prices significantly weighs down the stock of claims without necessarily increasing external financings. It mechanically lowers the intermediation ratio. Accordingly, the progression of stock market capitalization is often perceived, wrongly, as the sign of direct finance rising and intermediation declining. -The incidence is symmetrical in the case of decreasing securities prices. Indeed, on the other side, when stock market capitalization declines, the denominator of the ratio declines too. But depreciation effects should not be assimilated to a rebound of intermediate financings.

In short, increases in stock market prices lead to the overestimation of the relative importance of market financings and to the underestimation of intermediate financings. In both cases, one would err in not distinguishing between the two distinct tendencies that cause variation in stock market capitalization: on the one hand, an increase or decrease in the issuance of shares (volume effect) and, on the other hand, price appreciation or depreciation of the older shares (price effect).

When the intermediation ratio is computed with market values, it is impossible to distinguish between the "price effect" and the "volume effect". Therefore, in this study, intermediation ratios are calculated on a book value basis. Data from SNA68 are given in book value until 1994:4 and in market value since. Data (loans, bonds, and shares -both quoted and unquoted) from SNA93 are given in market value (see Table 2). To adopt a simple rule, we construct for stocks3 , a series estimated in book value by accumulation of flow data, whatever the period considered. 4 There is just one exception to the rule for the period 1997:4 to 2003:4. Indeed, for this period, the Bank of Japan provides detailed data of FFA with book value. Moreover, for the same period, we use also book value for data on loans.5 1970-1994: book value ; 1994-1998: market value market value

Sources of book value data

Accumulation of flow data Accumulation of flow data and "detailed data of FFA" from Dec. 1997to Dec. 2003 (P) 3. Results: Intermediation remains preponderant in Japan, but its nature is changing

Credit intermediation and market intermediation 6

Between 1970 and 2000, external financings granted to the Japanese domestic non-financial sector were multiplied by 18 (see Figure 2). This increase was mostly favourable to securities issues. This part in the external financings has more than doubled, from 15% to 30%. In the same time, banking credits decreased from 76% to 58% and the proportion of shares and other equities has remained stable around 10%.

But it is necessary to underline the stability of the large ratio. It contrasts with the reduction of the strict ratio which is accentuated at the beginning of the 1990s (see Figure 3). 7 The two components of the large ratio, namely the strict ratio (banking credits as a fraction of external financings) and the market intermediation ratio (claims investments by the FI as a fraction of external financings) each follow a perfectly symmetrical trend. The more the strict ratio has dropped, at the beginning of the 1990s, the more the market intermediation ratio has increased.

The rise of the market intermediation ratio translates the increasing share of the banking financial institutions and especially non-banking in the purchases of claims. In 1970 about half of the claims issued (10% of the 23% of external financings that they represented then) were acquired by the non-financial sector. It was just over one quarter in 1980. This fraction represented no more than one fifth between 1990 and 20008 (see Figure 4). In 2000, more than 70% of the market financings were actually intermediate financings (precisely 30% of the 42% of financings by issue of claims).

Thus, the intermediation degree of the financings has remained stable in Japan thanks to the increase in securities investments by the FI, compensating as such the fall bank credit.

Intermediation ratio of institutional sectors

The ratios of institutional sectors are in accordance with the global ratio. Indeed, the intermediation ratio of funds raised by NFC and the ratio of funds raised by GOV have a common feature. In both cases, the share of credit in the external financing decreases, while the share of securities issuances (by both NFC and GOV) acquired by the FI increases.

For NFC (Figure 5a), the large intermediation ratio decreases slightly. The diminution is more relevant in the computation made with the SNA 93 database than in the one with the older SNA 68. 9 The strict ratio started to fall by the end of the 1980s and kept decreasing significantly from during the 1990s. Concurrently, the share of securities issued by NFC and acquired by FI increased from the end of 1980s. But it was not sufficient to compensate for the fall in bank credits. In consequence, the global ratio fell. However, one can note that the decrease in the global ratio does not exceed 10 points (between 1980 and 2000). This result casts doubt on the idea that the firms' dependence vis-à-vis bank financing or more widely intermediate financing would have appreciably decreased in Japan [START_REF] Hoshi | The Japanese Banking Crisis: Where did it come from and how will it end?[END_REF]. Because of the close relation between market financings and intermediate financings, a securities issuance by a firm does not necessarily mean that this financing excludes banks or other financial intermediaries.

For GOV (Figure 5b), the global ratio was maintained at around 80%. It has even progressed if we consider calculations made with the SNA 93 database. This evolution can be explained by the increase in FI investments in claims issued by public firms. This fraction becomes greater and greater in external financings granted to GOV.

The intermediation ratio and stock exchange valuation

In Europe and in the United States, the 1990s were a period of a very dynamic stock markets, so much so that a speculative bubble formed and then burst at the beginning of the 2000s. Starting from computations in market values (of stock market capitalization, of credit over external financings, etc.), one would find in this context a disintermediation of financing. The explanation is that a measurement made from market value does not allow one to distinguish between the increase in the supply of stock of the market financings (stock effect) and the valorization effects (price effect). Therefore, by mechanically decreasing the relative share of the intermediate financings, stock exchange valorization masks the true evolution of the latter (a relative stability in the case of Europe, see Capelle-Blancard and Couppey-Soubeyran 2003). In Japan, like everywhere else, the 1980s was marked by the Big Bang and many financial innovations, whereas the 1990s were a period of a huge financial crisis. An analysis based on market value during this later period would lead to the fallacious conclusion that intermediation rebounded. But the stock market crisis in Japan was not favorable to intermediation, as the expansion of capital markets at the same time in Europe did not involve the fall of intermediation. In Europe, the decrease in the intermediation ratios in market value, due to the stock price increase, has been assimilated wrongly to a disintermediation of financing. In Japan, stock price decrease (notably observed during 1990-1992 and 2000-2003) had hidden the contraction in intermediate financing consecutive to the banking crisis. Our aim is not only to draw attention to obvious differences in absolute levels between market value and book value or to the more erratic evolution of the market value, but also to emphasize the deformation of the tendencies. Contraction of bank credit (see Figure 6) is dissimulated if we are only to consider market value measurement during 2000-2003 (the market value ratio remains stable and the book value ratio decreases). The trend is even fully inversed during 1990-1992 (the market value ratio increases and the book value ratio remains stable). Finally, the global intermediation ratio has remained stable during the relevant period thanks to the increase of securities purchased by IF that has compensated for the contraction of bank credit. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Figure 5a. Japanese intermediation ratios -Domestic Non-financial Corporations

Intermediation ratios versus other indicators of the degree of financial intermediation

Many indicators are used to evaluate financial development or to measure the weight of financial and banking intermediation. There is no consensus about which one is the best. Therefore, it would be interesting to compare the measures we obtained in computing intermediation ratios to those obtained by other. [START_REF] Edey | An Assessment of Financial Reform in OECD Countries[END_REF], studying financial systems in the OECD countries during 1980-1990, use for instance: i) an indicator of the financial sector by dividing the total of securities issuances into the GDP; ii) a ratio of financial intermediation as a fraction of financial assets held by FI over the total of the financial assets held by all institutional sectors; iii) a ratio of banking intermediation as a fraction of financial assets held by banks over the financial assets of FI. 10 In the study of Rajan and Zingales (2003a), which deals with the changing character of European finance from the1980s, or in their other study (2003b) which concerns the evolution of the financial systems in about thirty countries (including Japan) during the twentieth century, three other indicators are notably used: iv) the ratio of bank loans to the private sector and GDP ; v) the ratio of commercial and savings bank deposits to GDP; vi) the ratio of stock market capitalization in Tokyo to GDP. A last indicator is sometimes used which, however, measures the dynamics of capital markets more than financial development itself: vii) the value of share trading, that is the total amount of transactions (Domestic & Foreign, including Investment Funds) in Tokyo and Osaka divided by GDP. 11 Finally, if we add to this list the three ratios we have computed in our study (the credit intermediation ratio, the market intermediation ratio, the large intermediation ratio), ten indicators can be compared (see Table 3). 12

The comparison of these various indicators calls for several observations:

-All these indicators do not say anything about the financing structure, especially when they are fractions of GDP. Most of them inform only of the evolution in the absolute level (dividing by GDP only allows standard sizes) of the intermediation and not about the possible structural deformation of the financing. The fraction of deposits or bank loans over the GDP is an indicator that better measures the dynamism of these two traditional banking activities than the relative importance of the banks compared to the capital markets in financing. In this respect, only measurements as fractions of intermediate financings (properly defined) to external financings (like those we computed) can be really called intermediation ratios.

-These indicators give a more or less broad vision of intermediation. Indicators like fraction of loans or deposits to GDP (4 and 5) give a narrow vision of intermediation, as the one that prevails in the strict intermediation ratio (1a). In dividing the securities portfolio of FI by those of all resident sectors (2) or by dividing the banks portfolio by those of all FI (3), one measures the relative contribution of the FI or of the banks to market financings (and not to the whole of the external financings) obtained by domestic non financial or financial agents (and not only domestic by non financial agents). Therefore ( 2) and (3) do not measure as do (1a), ( 1b) and (1c) the share of the intermediate financings in the total of the external financings granted by DNF. Among all these indicators, only the large intermediation ratio offers a precise measurement of the weight of the banks (considered at the same time in their traditional activity of credit and their activity of claims investment) and other FI in the external financing of the DNF.

-All these indicators do not capture the changing nature of intermediation. Indicators ( 4) and ( 5) seem to provide empirical evidence for the dynamism, or even the regular growth of the traditional banking activities (credits and deposits). This, to say the least, is rather paradoxical looking at 10 Allen and [START_REF] Santomero | What do financial intermediaries do?[END_REF] use also such indicators.

11 The ratio of the number of domestic companies whose equity is publicly traded in Tokyo and Osaka and the country's population in millions varies between 23.39 in 1990 and26.21 in 2003 (source: FIBV). The ratio of funds raised through public equity offerings (both initial public offerings and seasoned equity issues) by domestic companies to gross fixed capital formation cannot be computed because required data are not available for Japan in the FIBV database.

12 Results presented in the table are quite different from those obtained by [START_REF] Edey | An Assessment of Financial Reform in OECD Countries[END_REF] and Rajan and Zingales (2003b). [START_REF] Edey | An Assessment of Financial Reform in OECD Countries[END_REF] use the OECD database. Rajan and Zingales (2003b) use data from [START_REF] Mitchell | International Historical Statistics[END_REF]: deposits and national income are extrapolated to 1999 for deposits by using the growth rate of deposits from the IMF's International Financial Statistics.

the analyses that stress the gravity of the banking crisis in Japan during 1990-2000. The breakdown of the total intermediation ratio (1c) into a credit intermediation ratio (1a) and a market intermediation ratio (1b), computed from book value data, more finely shows the contraction of credit and the correction enabled by the increase in market intermediation.

-One notes the erratic evolution of the indicators which integrate stock market data and which are particularly sensitive to changes in securities prices. These indicators (7 and 8) have to be interpreted with prudence because they provide an erratic measure of financial development.

Moreover, these indicators are not exclusive of those referring to intermediation. In other words, the evolution of intermediation cannot be inferred by the evolution of the stock price index or, more broadly, of market activity. Notes: Credit intermediation ratio = loans / external financing; Market intermediation ratio = securities held by financial intermediaries / external financing; Large intermediation ratio = Credit intermediation ratio + Market intermediation ratio. BoJ: SNA 68 for 1970 and 1980; SNA 93 for 1990 and 2000. Bank loan to the private sector is the ratio of claims on private sector of deposit money banks (IFS line 22d) and GDP (IFS line 99b). Deposits over GDP is the ratio of demand, time and savings deposits of deposit money banks (IFS lines 24, 25) and GDP. Stock market capitalization to GDP is the aggregate market value of equity of domestic companies listed on the Tokyo Stock Exchange (FIBV) divided by GDP. Value of share trading is the total amount of transactions (Domestic & Foreign, including Investment Funds) in Tokyo and Osaka divided by GDP. The financial intermediation ratio is the ratio of financial assets of financial institutions (including banks) to financial assets of all domestic sectors. The bank intermediation ratio is the ratio of assets of the banking sector to assets of all financial institutions.

Conclusion

According to our results, the intermediation ratio has remained quite stable, at around 85%. However, this stability is the result of two opposite trends: a contraction of credits and an increase in financial securities owned by financial (mostly, non banking) institutions. These two opposite trends would not have appeared if we had used market value data. Indeed, in this case, one would show in periods of a strong fall of the Japanese stock market (early 1990s and early 2000s) an increasing (actually artificial) intermediation ratio. Similarly, a computation using market value would have emphasized an artificial disintermediation during the sharp rise in the Japanese stock market from 1980 to 1990.

At the methodological level, one of the principal general results of this study is that the measurement of intermediation ratios must be done on the basis of stocks (more adapted than flows to the analyses of long periods) corrected for stock market valuation. In this respect, a generalization of accounting standards concerning market value will make the measurement of the financings structures even more delicate, or in any case a treatment of the data would have to be systematic. Moreover, the measurement of the financial system structure must be made with intermediation ratios that precisely measure the fraction of intermediate financings over external financings offered to DNF. Other ratios of financials assets or banking activity ratios as a fraction of GDP measure the dynamism of the banks or other FI better than their relative importance in the financial system. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 -8,5 -8,0 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 Notes. Deposits to GDP is the ratio of commercial and savings deposits divided by GDP. Source: [START_REF] Rajan | Banks and Markets: The changing Character of European Finance[END_REF]. Notes. Stock market capitalization to GDP is the ratio of the aggregate market value of equity of domestic companies to GDP. Source: [START_REF] Rajan | Banks and Markets: The changing Character of European Finance[END_REF]. Notes. Financial intermediation ratio is the ratio of financial assets of financial institutions (including banks) to financial assets of all domestic. Source: [START_REF] Edey | An Assessment of Financial Reform in OECD Countries[END_REF], OECD Financial Accounts. Notes. Bank intermediation ratio is the Ratio of assets of banking sector to assets of all financial institutions. Source: [START_REF] Edey | An Assessment of Financial Reform in OECD Countries[END_REF], OECD Financial Accounts.

Figure

  Figure 1. Outstanding amounts of stocks issued by Japanese corporations

Figure 6 .

 6 Figure 6. Strict intermediation ratios with book or market data

Finally, on a

  theoretical level, by showing that intermediation financing increasingly consists in the purchasing of claims by IF, our study confirms the close connection between market financings and intermediate financings. Notions traditionally used to analyze the evolution of financial systems thus appear less and less operational. It is particularly relevant with regard to the distinction between market (or direct) financing and intermediate (or indirect) financing, and that between a bank-based system and a capital market-based system. Market financings are to a large extent intermediate financings and the orientation of the financial systems is basically mixed.

  intermediation ratio = (loans + securities hold by financial intermediairies) / external financing. Strict intermediation ratio = loans / external financing. Outstanding on a market value basis.

Figure

  Figure A2. Gross versus net data

  intermediation ratio = (loans + securities hold by financial intermediairies) / external financing. Strict intermediation ratio = loans / external financing. SNA68: quarterly data from 1970:4 to 1999:1. SNA 93: annual data from FY1989 to FY1997 and quarterly from 1998:1 to 2003:4. Outstanding on a book value basis.

Figure

  Figure A3. Evolution of different indicators of financial development in Japan

Figure

  Figure A4. Evolution of Japanese financial assets ratios

Figure

  Figure A6. Evolution of stock market capitalization over GDP

Figure

  Figure A7. Evolution of financial intermediation ratio

Figure

  Figure A8. Evolution of bank intermediation ratio

Table 1 . Institutional sectors, financial assets and liabilities Institutional sectors SNA 68 SNA 93

 1

			Banks (domestically licensed banks, foreign-
	Banks	Banks	owned banks, financial institutions for agriculture, forestry, and fisheries, financial
			institutions for small businesses)
	Insurances & Pension Funds	(Private) Insurances	Insurances + Pension Funds

Financial intermediaries (FI) without BoJ Non-bank financial intermediaries Other financial intermediaries

					Trusts + Public financial institutions + Securities companies	Postal Saving + Collectively managed trusts + Other financial intermediaries + Financial auxiliaries (financial institutions other than intermediaries)
	Domestic	non-financial	(DNF)	Households Nonfinancial corp. (NFC) General government (GOV)	Personal Corporate business Government	Households Non-financial corporations General government
	Overseas		Overseas	Overseas
	Fund-raising	SNA68	SNA93
	Loans			Loans	Loans
	Securities		Bonds, bills and commercial papers	Securities other than share
					Stocks	Share and other equities

Table 2 . Characteristics of the data SNA68 SNA93 Periods and frequencies

 2

Quarterly: March 1970 to March 1998 Annually: FY1989 to FY 1997; Quarterly: March 1998 to December 2003 (P) Initial stock data

Fund-raising by the Japanese domestic nonfinancial sector (100,000 JPY and %)

		Figure 2. Japanese External Financing -Domestic Nonfinancial Sector
	18 000 1970													1980
	16 000		Shares and other equities Securities other than shares Loans via non-bank FI; 232; 21%			Debt securities via banks; 66; 6%					Loans via non-bank FI; 1 254; 25%	Debt securities via banks; 509; 10%
	14 000									Debt securities via
										non-bank FI; 44;
	10 000 12 000						252; 23%		43; 4% 4% Debt securities via DNF;					1 508; 30%	Debt securities via non-bank FI; 401; 8%
	8 000	Loans via banks; 598; 56%						Shares via non-bk FI; 10; 1% Shares via banks; 8; 1%				Loans via banks; 2 213; 45%	Debt securities via DNF; 321; 6%
										Shares via DNF;
	6 000										66; 6%							Shares via banks; 35; 1%
																		Shares via non-bk FI; 42; 1%
																		Shares via DNF; 107;
	4 000									Overseas; 14; 1%						Overseas; 93; 2% 2%
	0 2 000 1990		bank FI; 2 832; Loans via non-					Debt securities via banks; 874; 7%		2000			Debt securities via banks; 1 429; 8%
		1970		1975 23%		1980		1985		1990		1995	2000 Loans via non-
																bank FI; 4 863;
																	28%
						3 783; 31%			Debt securities via non-bank FI; 1 367; 11%					7 275; 42% Securities	Debt securities via non-bank FI; 2 867; 16%
	90% 100%	Debt securities via DNF; 559; 5% (Loans + Securities held by FI) / External Financing Loans via banks; 5 Shares via non-bank FI; 260; 2% 661; 46% Shares via banks; 123; 1%				Loans via banks; 5 386; 31%	Debt securities via DNF; 1 226; 7% Shares via banks; 149; 1% Shares via non-bk FI; 288; 2%
	80%									Shares via DNF; 139; 1% Overseas; 461; 4%						Shares via DNF; 885; 5%
	70%																	Overseas; 430; 2%
	60%	Loans / External Financing
	50%
	40%
	30%
		Securities held by FI / External Financing
	20%
	10%
	0%
	1 9 7 0	1 9 7 2	1 9 7 4	1 9 7 6	1 9 7 8	1 9 8 0	1 9 8 2	1 9 8 4	1 9 8 6	1 9 8 8	1 9 9 0	1 9 9 2	1 9 9 4	1 9 9 6	1 9 9 8	2 0 0 0	2 0 0 2

Market development did not reduce the share of the intermediate financings in the total of the external financings. But it changed the nature of the latter. Intermediate financings became more and more securities investments, therefore market financings. Intermediates financings and market financings appear increasingly integrated. Loans Data: Flow of Funds Account -Bank of Japan. Outstanding on a book value basis. SNA68 from 1970 to 1995 and SNA93 for 2000.

Figure 3. Japanese intermediation ratios -Domestic Non-financial Sector Notes. From top to bottom: 1) Large intermediation ratio = (loans + securities held by financial intermediaries) / external financing; 2) Credit intermediation ratio = loans / external financing; 3) Market intermediation ratio = securities held by financial intermediaries / external financing. Data: Flow of Funds Account -Bank of Japan. Outstanding on a book value basis. SNA68 (solid line): quarterly data from 1970:4 to 1999:1 and SNA93 (dotted line): annual data from FY1989 to FY1997 and quarterly from 1998:1 to 2003:4.

Figure 4.

Table 4 . Various Indicators of the Japanese Financial System

 4

		Data	1970	1980	1990	2000
		source
	1a) Credit intermediation ratio	BoJ	77%	70%	68%	58%
	1b) Market intermediation ratio	BoJ	12%	20%	17%	27%
	1c) Large intermediation ratio	BoJ	89%	89%	85%	85%
	2) Financial assets of
	FI/financial assets of all domestic sector	BoJ	52%	60%	47%	60%
	3) Financial assets of banking sector/financial assets of FI	BoJ	49%	48%	34%	31%
	4) Deposits/GDP	IFS	67%	78%	104%	111%
	5) Bank Loan to Private Sector/GDP	IFS	77%	83%	119%	113%
	6) Securities issued by DNF/GDP	BoJ, IFS	97%	196%	267%	540%
	7) Stock market capitalization/GDP	FIBV, IFS	_	_	89%	71%
	8) Value of share trading/GDP FIBV, IFS	_	_	47%	59%

Table A : Japanese financial intermediation ratios

 A

		1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986
	External financing	1,082	1,119	1,165	1,219	1,304	1,352	1,399	1,482	1,601	1,678	4,976	5,103	5,230	5,361	5,559	5,670	5,805
	Loans	76.74	77.91	78.11	79.17	78.95	77.77	76.37	74.34	71.59	70.69	69.66	68.76	68.60	68.37	68.18	68.00	68.02
	Loans via Banks	55.27	56.18	56.95	57.55	56.17	54.03	51.92	50.07	47.80	46.11	44.47	43.56	43.38	43.21	43.52	43.98	44.81
	Loans via Non-Bank Financial Intermediaries	21.47	21.73	21.16	21.62	22.78	23.74	24.45	24.27	23.79	24.58	25.20	25.21	25.22	25.16	24.66	24.02	23.21
	Securities other than share	14.70	14.35	14.82	14.30	15.01	16.64	18.52	20.86	23.92	25.03	26.22	27.17	27.37	27.74	27.94	28.15	28.15
	via Banks	6.11	5.82	6.10	5.70	5.92	6.75	7.79	8.55	10.00	10.43	10.22	10.30	9.70	9.20	8.78	8.39	8.18
	via Insurances & Pension Funds	0.34	0.27	0.27	0.26	0.32	0.40	0.51	0.69	0.94	1.08	1.08	1.14	1.24	1.30	1.35	1.37	1.56
	via Other Financial Intermediaries	3.75	3.84	4.32	4.29	4.50	4.48	4.46	5.38	6.06	6.25	6.97	7.28	7.86	8.44	9.09	9.59	10.57
	via Domestic Nonfinancial sector	3.97	3.93	3.70	3.72	3.93	4.46	5.00	5.27	5.77	6.06	6.46	6.99	6.91	7.08	6.90	6.67	5.70
	via Overseas	0.51	0.49	0.43	0.32	0.35	0.54	0.75	0.96	1.13	1.18	1.46	1.44	1.61	1.67	1.75	2.04	2.04
	Share and other equities	8.56	7.75	7.07	6.53	6.04	5.59	5.10	4.80	4.48	4.29	4.11	4.06	4.02	3.89	3.89	3.86	3.82
	via Banks	0.73	0.72	0.83	0.84	0.81	0.75	0.70	0.70	0.68	0.72	0.71	0.72	0.72	0.69	0.72	0.73	0.68
	via Insurances & Pension Funds	0.68	0.66	0.71	0.72	0.70	0.67	0.64	0.66	0.64	0.64	0.60	0.59	0.59	0.55	0.55	0.56	0.57
	via Other Financial Intermediaries	0.27	0.27	0.30	0.28	0.28	0.30	0.32	0.34	0.37	0.32	0.24	0.24	0.22	0.24	0.34	0.46	0.77
	via Domestic Nonfinancial sector	6.13	5.46	4.67	4.38	4.10	3.61	3.17	2.96	2.60	2.41	2.15	1.98	1.93	1.61	1.54	1.42	1.11
	via Overseas	0.74	0.63	0.56	0.32	0.16	0.26	0.27	0.15	0.19	0.19	0.41	0.54	0.56	0.80	0.73	0.69	0.69
	Large intermediation ratio	88.63	89.47	90.64	91.26	91.46	91.12	90.79	90.65	90.29	90.12	89.49	89.01	88.94	88.79	89.00	89.09	90.35
	Market Intermediation ratio	11.89	11.57	12.53	12.09	12.51	13.34	14.41	16.31	18.70	19.43	19.83	20.25	20.34	20.42	20.83	21.09	22.33
	via Insurances & Pension Funds	1.52	1.61	1.48	1.35	1.40	1.64	2.00	2.61	3.27	3.43	3.63	6.53	7.15	7.56	8.18	8.94	9.37
	via Other Financial Intermediaries	10.81	10.89	10.45	9.77	10.12	10.67	11.35	11.74	12.60	13.66	14.23	8.28	9.00	8.80	8.81	9.40	9.78
	via Domestic Nonfinancial sector	4.99	4.42	3.92	4.55	4.22	3.88	3.25	3.42	3.63	3.55	3.47	7.27	7.24	7.00	7.54	7.28	7.75
	via Overseas	2.27	2.09	2.69	3.40	3.66	3.37	2.96	2.73	2.34	2.71	3.14	1.74	1.28	1.42	1.39	1.23	1.19
	Share and other equities	3.92	4.12	4.62	4.60	4.50	4.36	4.27	4.18	4.09	4.08	4.12	7.85	8.08	8.58	8.85	8.99	8.79
	via Banks	0.67	0.74	0.86	1.00	1.02	1.03	0.99	0.99	1.00	0.99	1.05	0.92	0.79	0.85	0.84	0.79	0.82
	via Insurances & Pension Funds	0.61	0.68	0.75	0.87	0.93	0.95	0.93	0.89	0.82	0.80	0.80	1.09	0.87	0.94	1.01	0.97	0.91
	via Other Financial Intermediaries	1.07	1.20	1.44	1.24	1.09	0.94	0.97	0.94	0.88	0.90	0.96	0.53	0.55	0.71	0.85	0.91	0.84
	via Domestic Nonfinancial sector	1.20	1.07	1.02	1.13	0.99	0.97	0.82	0.68	0.62	0.52	0.51	4.62	4.75	5.05	5.14	5.35	5.09
	via Overseas	0.37	0.44	0.57	0.35	0.48	0.47	0.56	0.68	0.79	0.89	0.81	0.69	1.12	1.03	1.01	0.98	1.13
	Large intermediation ratio	91.06	91.88	91.72	90.49	90.58	91.25	92.35	92.42	92.87	92.28	92.05	85.69	85.60	85.50	84.91	85.16	84.84
	Market Intermediation ratio	22.71	23.32	22.83	21.36	20.85	21.52	22.61	23.45	24.80	25.76	26.48	23.14	25.33	27.01	27.84	29.70	32.03

Notes: Credit intermediation ratio = loans / external financing; Market intermediation ratio = securities hold by financial intermediaries / external financing; Large intermediation ratio = Credit intermediation ratio + Market intermediation ratio. Data: Flow of Funds Account -Bank of Japan. Outstanding on a book value basis.

SNA68 from 1970[START_REF] Schmidt | Disintermediation and the Role of Banks in Europe: An International Comparison[END_REF] and SNA93 1998 to 2003

. Units: External financing × JPY 100,000 millions or % of external financing. Quarterly results available on request.

ADDITIONAL APPENDIX The biggest capital markets in the world in 2003 Market capitalization a) Market capitalization of domestic shares newly listed Value of share trading - domestic companies

  : World Federation of Exchange. USD millions converted with average year exchange rates. a) The others stock markets have a market capitalization and a value of share trading for domestic companies below USD 1,000,000 millions. b) Data for Deutsche Börse and Euronext are not available.

	Figure A1.
	NYSE	11,328,953	Tokyo	75,659	NYSE	8,778,301
	Tokyo	2,953,098	NYSE	70,709	Nasdaq	6,703,349
	Nasdaq	2,844,193	Korea	67,467	London	2,143,317
	London	2,460,064	Borsa Italiana	42,462	Tokyo	2,092,141
	Euronext	2,076,410	London	37,173	Euronext	1,911,185
	Deutsche Börse	1,079,026	Nasdaq	31,663	Deutsche Börse 1,200,895
	Value of private bonds listed b) Value of public bonds listed
	London	823,632	Osaka	4,422,407
	Copenhagen	305,432	Borsa Italia	1,340,755
	Korea	297,676	NYSE	1,005,000
	Wiener Börse	294,030	London	464,162
	NYSE	228,000	India	252,529
	Luxembourg	131,556	Korea	212,013

Source

Japanese market intermediation ratios on a market value basis and stock index

	80%
	75%
	70%
	65%
	60%
	55%
	50%
	45%
	40%

Because broken down accounts are not available to identify the stocks of claims hold by FI over each sector (NFC, GOV and Overseas), we assume that the fraction of a given category of claims, issued by DNF and purchased by a given category of FI, is supposed to be equal to the fraction of the same category of claims held by the FI considered, whatever the issuer. If, for instance, insurance companies and pension funds hold α% of the total stocks of bonds, the hypothesis is made that they hold in the same manner α% of the stock of bonds issued by NFC, α% of

Price effects also exist for bonds, but to a much lesser extent, so we limit the correction to stocks.

To consider a sum of flows is required for long-term studies since flows series are too erratic. Note that another method is possible to neutralize the price effects. It consists in dividing stock market capitalization with a price index. The choice of a stock price index (it is for instance the approach chosen by[START_REF] Rousseau | Equity Markets and Growth: Cross-Country Evidence on Timing and Outcomes, 1980-1995[END_REF] to adjust the indicators of financial development proposed by[START_REF] Levine | Stock Markets, Banks, and Economic Growth[END_REF] from price effects) poses problems of representativeness and does not allow one to take account of the valuation of non-listed shares. It is then preferable to determine a price index computed with flow and stocks data (see[START_REF] Capelle-Blancard | Le financement des agents non financiers en Europe : le rôle des intermédiaires financiers demeure prépondérant[END_REF].

5 The Bank of Japan also provided a document concerning the Japanese channels of fund-raising for the period 1989FY to 2002FY. There are some differences with our own estimates. For loans, the difference is between -5.5% and -1.9%. Differences are higher for securities issued by the domestic non-financial sector and held by the financial sector: from -16% in 1989FY to -2% in 2002FY. That leads to a maximum difference of 4 and 7 points for the strict and the large intermediation ratios, respectively.

Detailed results are presentedTable A in the Appendix.

There is, of course, a little difference between SNA 68 and SNA 95, but one notes that the trends are the same.

Data from 1970 to 1990 come from SNA 68 whereas those of 2000 come from SNA

We checked that cross-shareholdings between Japanese NFCs do not affect our results. To do so, we computed net shares outstanding (assets minus liabilities of NFC shares). Intermediation ratios are, obviously, lower but results are qualitatively the same. Data are available on request.