Convolutional neural networks applied to high-frequency market microstructure forecasting

Highly sophisticated artificial neural networks have achieved unprecedented performance across a variety of complex real-world problems over the past years, driven by the ability to detect significant patterns autonomously. Modern electronic stock markets produce large volumes of data, which are very suitable for use with these algorithms. This research explores new scientific ground by designing and evaluating a convolutional neural network in predicting future financial outcomes. A visually inspired transformation process translates high-frequency market microstructure data from the London Stock Exchange into four market-event based input channels, which are used to train six deep networks. Primary results indicate that con-volutional networks behave reasonably well on this task and extract interesting microstructure patterns, which are in line with previous theoretical findings. Furthermore, it demonstrates a new approach using modern deep-learning techniques for exploiting and analysing market microstructure behaviour

Evolutionary data selection for enhancing models of intraday forex time series

The hypothesis in this paper is that a significant amount of intraday market data is either noise or redundant, and that if it is eliminated, then predictive models built using the remaining intraday data will be more accurate. To test this hypothesis, we use an evolutionary method (called Evolutionary Data Selection, EDS) to selectively remove out portions of training data that is to be made available to an intraday market predictor. After performing experiments in which data-selected and non-data-selected versions of the same predictive models are compared, it is shown that EDS is effective and does indeed boost predictor accuracy. It is also shown in the paper that building multiple models using EDS and placing them into an ensemble further increases performance. The datasets for evaluation are large intraday forex time series, specifically series from the EUR/USD, the USD/JPY and the EUR/JPY markets, and predictive models for two primary tasks per market are built: intraday return prediction and intraday volatility prediction

Modeling dynamic volatility under uncertain environment with fuzziness and randomness

The problem related to predicting dynamic volatility in financial market plays a crucial role in many contexts. We build a new generalized Barndorff-Nielsen and Shephard (BN-S) model suitable for uncertain environment with fuzziness and randomness. This new model considers the delay phenomenon between price fluctuation and volatility changes, solves the problem of the lack of long-range dependence of classic models. Through the experiment of Dow Jones futures price, we find that compared with the classical model, this method effectively combines the uncertain environmental characteristics, which makes the prediction of dynamic volatility has more ideal performance

Recurrent Neural Networks with more flexible memory: better predictions than rough volatility

We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or with highly disparate time scales. We compare the ability of vanilla and extended long short term memory networks (LSTMs) to predict asset price volatility, known to have a long memory. Generally, the number of epochs needed to train extended LSTMs is divided by two, while the variation of validation and test losses among models with the same hyperparameters is much smaller. We also show that the model with the smallest validation loss systemically outperforms rough volatility predictions by about 20% when trained and tested on a dataset with multiple time series.Comment: 9 page

Markov Models on Share Price Movements in Nigeria Stock Market Capitalization

The stock markets trends can impact companies in different ways. The rise and fall of share price values affect a company?s market capitalization and therefore its market value, and are exposed to market risk. This study assumed share volatility as a stochastic process with Markov property. Thus proposed a first order, time homogenuous Markov chain model for trend prediction of two banks; that is Fidelity bank and Access bank closing share prices from 1-4-2016 to 23 -03-2022. The prediction was done by establishing three states that exist in stock price change which are share prices increase, decline (decrease) or steady. The transition matrix was generated. The powers of transition matrices and probability vectors were also generated for some years and equilibrium was attained

Impact of Network Connectedness

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 이재욱.금융 자산은 언제나 리스크에 노출되어 있다. 이 리스크의 크기와, 각 자산이 리스크에 대해 얼마나 보상받는 지를 정확히 측정하는 것은 자산의 특성을 이해하는 데 중요한 문제이다. 자산가격결정모형 (asset pricing model)은 자산의 리스크와 그 보상을 통해서 금융 자산의 수익률을 설명하려 하는 모형이다. 본 연구에서는 여러 자산가격결정모형의 형태 중 팩터 모델에 집중하였다. 팩터 모델은 초과 수익률을 팩터와 베타로 분리해서 설명하는 모델이다. 전통적인 팩터 모델들은 거시 금융 변수나 기업 변수 등을 통하여 팩터와 베타를 추정하는데, 이 때 자산 간의 연결관계를 고려하는 연구는 많이 진행되지 않았다. 금융 자산들은 서로 영향을 주는 관계에 있기 때문에 각각의 수익률 또한 개별적이 아니라 자산 간의 그래프 구조를 고려하며 동시에 평가되어야 한다. 본 논문은 팩터 모델에 자산 간의 연결 구조를 반영하기 위한 인공지능 기반 실증적 자산가격결정모형을 제안한다. 이를 위해 먼저 그래프 인공신경망 (GNN)을 바탕으로 한 멀티 팩터 모델을 개발하였다. 이 때 모델의 구조를 결정하는 것 만큼이나 중요한 것은 자산 간 그래프 구조를 어떻게 정의할 것인가라는 문제이다. GNN은 그 입력 변수로서 잘 정의된 그래프 구조를 요구하지만 자산 간의 연결 구조는 명확하게 정의되지 않았기 때문에, 본 연구에서는 자산 간의 연결성을 피어슨 상관계수를 이용하여 추정하고 이를 특정 임계값을 통해 0과 1로 이진화 시키는 방식을 사용했다. 제안한 모델의 구조는 베타를 추정하는 부분과 팩터를 추정하는 부분으로 나뉘어지는데, 각각 기업 변수와, 수익률을 이용해서 추정한다. 1957년부터 미국에 상장된 주식들을 대상으로 한 실증 실험 결과, 제안한 모델은 설명력과 예측 성능 측면에서 벤치마크 모델들보다 우수한 성능을 보였다. 또한 통계적 성능 이외에도 팩터의 경제적 의미를 측정하는 면에서, 제안한 모델로부터 추정한 팩터가 가장 효율적인 확률적 할인요소 (stochastic discount factor)를 추정할 수 있다는 점 역시 확인하였다. 자산가격결정모형의 가장 중요한 목적은 수익률이지만, 변동성 또한 금융 자산의 움직임을 설명하는 데 중요한 성질이다. 많은 사전 연구에서 밝혀졌듯 수익률과 변동성 사이에는 상관관계가 존재하기 때문에 변동성은 수익률을 설명하는 요인이 될 수 있다. 자산가격결정모형에서와 마찬가지로 자산들 간의 연결 구조를 고려하는 것은 변동성 예측에서도 성능 향상에 큰 영향을 미칠 수 있다. 변동성 분석에서는 여러 자산의 변동성이 서로 영향을 미치는 것을 스필오버 (spillover)라 부른다. 본 논문에서는 스필오버 효과를 직접적으로 반영하는 변동성 예측 모델을 개발하였다. 제안한 모델은 변동성의 측면에서 자산 간 연결 구조를 변동성 스필오버 지수로 구성한 인접행렬로 정의하며, 모델의 구조로는 시공간적 그래프 인공신경망 (spatial-temporal GNN)를 사용하였다. 글로벌 시장 지수들에 대한 실증 실험을 통해서 제안한 모델은 단기와 중기 변동성 예측에서 벤치마크 모델에 비해 가장 좋은 예측 성능을 보이고, 다른 시장에 큰 영향을 주는 시장을 이용하여 다른 시장에 대한 예측 성능을 크게 높일 수 있음을 보였다. 자산가격결정모형에 변동성을 직접적으로 반영하기 위해서는 모형 내에서 변동성이 어떻게 정의되는가를 먼저 살펴보아야 한다. 변동성은 자산가격결정모형 내에서 잔차의 표준편차로 해석할 수 있다. 그러나 시계열 기반 방법론을 사용하여 추정하는 기존의 자산가격결정모형은 시간에 따라 불변하는 변동성을 가정한다. 본 논문에서는 시간에 따라 변하는 변동성을 예측 모델을 이용하여 추정하고, 이를 팩터 모델의 손실함수에 정규화로 사용함으로써 시간에 따라 변화하는 변동성의 특성을 반영하는 팩터 모델을 제안하였다. 미국 상장 주식에 대한 실증 실험 결과 제안한 모델은 시간 불변 변동성 조건을 완화하지 않은 모델에 비해 변동서이 낮은 시기에서 통계적 성능이 큰 폭으로 상승함을 확인하였다. 현재 무시할 수 없는 규모로 성장한 가상화폐 시장에는 구조적으로 확실하게 연결된 자산이 존재한다. 같은 블록체인 상에 존재하는 토큰들은 해당 블록체인 위에서 발행되고 거래되므로 네트워크 구조 상으로 연결성을 지닌다. 본 연구에서는 앞서 진행된 연구에 대한 응용으로, 명확히 구조적으로 연결된 자산들이 초과 수익률을 설명할 수 있는 측정 가능한 공통된 팩터를 가짐을 보이고자 했다. 연구의 대상을 이더리움 블록체인 상의 토큰들로 제한하여 실증 실험을 진행한 결과, EIP-1559 적용 이후에 이더리움 가스 수익률이 시장 수익률과 함께 토큰의 수익률을 설명할 수 있는 팩터로서 작용함을 보였다. 또한, 이더리움 가스 수익률은 토큰의 변동성에 영향을 주는 요소로, 토큰 변동성 예측에도 도움을 줄 수 있는 요소임을 스필오버 기반 변동성 예측 모델을 통해 확인하였다. 본 논문은 자산 간의 연결성을 고려한 자산가격결정모형을 구성하였으며, 이를 통해서 금융 자산들이 갖는 그래프 구조가 실질적으로 수익률에 영향을 미침을 확인할 수 있다. 이 연구결과는 향후 새로운 금융 시장에 대해서도 적용 가능한 확장성 있는 모델이며, 금융 자산의 평가에 있어 여러 자산을 동시에 상관관계를 고려하며 평가해야 한다는 함의점을 제공하고 있다.Financial assets are always exposed to risks. It is important to evaluate the risk properly and figure out how much each asset is compensated for its risk. Asset pricing model explains the behavior of financial asset return by evaluating the risk and risk exposure of asset return. We focused on factor model structure among asset pricing models, which explains excess return through factor and beta coefficients. While conventional factor models estimate factor or beta through various macroeconomic variables or firm-specific variables, there exist fewer studies considering the connectedness between assets. Since financial assets have connected dynamics, asset returns should be priced simultaneously considering the graph structure of assets. In this dissertation, we proposed the AI-based empirical asset pricing model to reflect the connected structure between assets in the factor model. We first proposed the graph neural network-based multi-factor asset pricing model. As important as the structure of the model in constructing an asset pricing model that reflects the structure of the connection between assets is, how to define the connectivity. Graph neural network requires a well-defined graph structure. We defined the connectedness between assets as the binary converted Pearson correlation coefficients of asset returns by the cutoff value. The proposed model consists of a beta estimation part and a factor estimation part, where each part is estimated with firm characteristics and excess returns, respectively. The empirical analysis of U.S equities reveals that the proposed model has more explanatory power and prediction ability than benchmark models. In addition, the most efficient stochastic discount factor can be estimated from the estimated factors. While return is the main object of asset pricing, volatility is also important property for explaining the behavior of financial assets. Volatility can be the factor in explaining return since many studies point out that return and volatility are correlated. As with the asset pricing model, considering the connected structure between assets in volatility prediction can be of great help in explaining the dynamics of assets. In the volatility analysis, what affects between volatility is called spillover. In this aspect, we proposed the volatility prediction model that can directly reflect this spillover effect. We estimated the graph structure between asset volatility using the volatility spillover index and utilized the spatial-temporal graph neural network structure for model construction. From the empirical analysis of global market indices, we confirm that the proposed model shows the best performance in short- and mid-term volatility forecasting. To include volatility in the asset pricing discussion, it is necessary to focus on how volatility is defined in the asset pricing model. In the asset pricing model, volatility can be interpreted as the variance of the residual of the model. However, asset pricing models with time-series estimation mostly have time-unvarying volatility constraints. We constructed an asset pricing model with time-varying volatility by estimating variability using the prediction model and reflecting it in the training loss of the asset pricing model. We identify that the proposed model can improve the statistical performance during the low volatility period through an empirical study of U.S equities. Currently, there are clearly structurally connected assets in the cryptocurrency market, which has grown to a scale that cannot be ignored. All of the same blockchain-based tokens are issued and traded on that blockchain, so they have strong structural connectivity. We tried to identify that an observable factor for explaining excess return exists in such connected tokens as an application of previous studies. We limited the analysis target to Ethereum-based tokens and showed that the Ethereum gas price became a factor for the macroeconomic factor model after the application of EIP-1559. Furthermore, we applied the volatility spillover index-based volatility prediction model using gas return and showed that gas return can increase the prediction performance of certain tokens' volatility.Chapter 1 Introduction 1 1.1 Motivation of the Dissertation 1 1.2 Aims of the Dissertation 10 1.3 Organization of the Dissertation 13 Chapter 2 Graph-based multi-factor asset pricing model 14 2.1 Chapter Overview 14 2.2 Preliminaries 17 2.2.1 Graph Neural Network 17 2.2.2 Graph Convolutional Network 18 2.3 Methodology 19 2.3.1 Multi-factor asset pricing model 19 2.3.2 Proposed method 21 2.3.3 Forward stagewise additive factor modeling 23 2.4 Empirical Studies 24 2.4.1 Data 24 2.4.2 Benchmark models 24 2.4.3 Empirical results 28 2.5 Chapter Summary 33 Chapter 3 Volatility prediction with volatility spillover index 37 3.1 Chapter Overview 37 3.2 Preliminaries 41 3.2.1 Realized Volatility 41 3.2.2 Volatility Spillover Measurements 42 3.2.3 Benchmark Models 45 3.3 Empirical Studies 50 3.3.1 Data 50 3.3.2 Descriptive Statistics 51 3.3.3 Proposed Method 52 3.3.4 Empirical Results 54 3.4 Chapter Summary 61 Chapter 4 Graph-based multi-factor model with time-varying volatility 64 4.1 Chapter overview 64 4.2 Preliminaries 67 4.2.1 Local-linear regression for time-varying parameter estimation 67 4.3 Methodology 68 4.3.1 Time-varying volatility implied loss function 68 4.3.2 Proposed model architecture 70 4.4 Empirical Studies 72 4.4.1 Data 72 4.4.2 Benchmark Models 72 4.4.3 Empirical Results 73 4.5 Chapter Summary 79 Chapter 5 Macroeconomic factor model and spillover-based volatility prediction for ERC-20 tokens 82 5.1 Chapter Overview 82 5.2 Preliminaries 85 5.3 Methodology 86 5.3.1 Relation analysis 86 5.3.2 Factor model analysis 89 5.3.3 Volatility prediction with volatility spillover index 90 5.4 Empirical Studies 90 5.4.1 Data 90 5.4.2 Empirical Results 98 5.5 Chapter Summary 102 Chapter 6 Conclusion 105 6.1 Contributions 105 6.2 Future Work 108 Bibliography 109 국문초록 130박

Volatility Indexes seem to point to the Past

In theory, by institutional trading options (wholesale), professional market participants asses and set future volatilities that can be identified for the retail using the Black-Scholes-formula in reverse. In reality, as regression analysis suggests, it is historical market data which instead are used to determine future values. Further analysis shows that historical volatilities are insufficient predictors. Yet this questionable practice is considered by international accounting standards (IAS/IFRS) to allow "historical data and implied volatilities" for "reasonable estimations". In a kind of short-circuit, historical volatilities are introduced into option trading and returned as implied volatility-indexes. In reality, both differ significantly from future values. Comparing the volatility of the past nine weeks with that of the following nine weeks, estimation error ranges from four to over ten percentage points. (No paper found in the net challenging the implied hypothesis of IAS 39/AG82(f)

Modelling returns volatility: mixed-frequency model based on momentum of predictability

The estimation and prediction of financial asset volatility are important in terms of theoretical and practical applications. Considering that low-frequency and high-frequency information plays an important role in volatility prediction, this article proposes a mixed-frequency model based on the momentum of predictability (MF-MoP). To illustrate the advantages of the proposed model, comparative research is conducted on the prediction accuracy of volatility among the GARCH model, the Realized GARCH model and the MF-MoP model, by the loss function and MCS test. The empirical results show that the MF-MoP model has higher prediction accuracy than the other two models; especially based on skewed-t distribution, the MF-MoP significantly outperforms the competing models. Moreover, the MF-MoP model can improve the forecasting of volatility, regardless of different lookback periods (including 1, 3, 6 and 9 days), different data (including the CSI 300 index, the N225 index and the KS11 index), and realized measures (including RV, RRV and MedRV), indicating that the model is robust

Long-term effects of the asymmetry and persistence of the prediction of volatility: Evidence for the equity markets of Latin America

Este artículo analiza el comportamiento de la volatilidad en los mercados accionarios de América Latina.This article proposes an extension to the CGARCH model in order to capture the characteristics ofshort-run and long-run asymmetry and persistence, and examine their effects in modeling and forecastingthe conditional volatility of the stock markets from the region of Latin America during the period from 2January 1992 to 31 December 2014. In the sample analysis, the estimation results of the CGARCH-classmodel family reveal the presence of short-run and long-run significant asymmetric effects and long-runpersistency in the structure of stock price return volatility. The empirical results also show that the use ofsymmetric and asymmetric loss functions and the statistical test of Hansen (2005) are sound alternativesfor evaluating the predictive ability of the asymmetric CGARCH models. In addition, the inclusion of long-run asymmetry and long-run persistency in the variance equation improves significantly the out of samplevolatility forecasts for emerging stock markets of Argentina and Mexico

A sentiment analysis approach to the prediction of market volatility

Prediction and quantification of future volatility and returns play an important role in financial modeling, both in portfolio optimisation and risk management. Natural language processing today allows one to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. We found that there is evidence of correlation between sentiment and stock market movements. Moreover, the sentiment captured from news headlines could be used as a signal to predict market returns; we also found that the same does not apply for volatility. However, for the sentiment found in Twitter comments we obtained, in a surprising finding, a correlation coefficient of -0.7 (p < 0.05), which indicates a strong negative correlation between negative sentiment captured from the tweets on a given day and the volatility observed the next day. It is important to keep in mind that stock volatility rises greatly when the market collapses but not symmetrically so when it goes up (the so-called leverage effect). We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modeling, based on Latent Dirichlet Allocation, in order to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modeling even on modest (essentially personal) architecture our classifier achieved a directional prediction accuracy for volatility of 63%