Forecasting limit order book price changes using change point detection

The main purpose of this thesis is to propose a method for using a change point detection algorithm to forecast short term limit order book price changes. The idea is to test whether a significant change of the shape of the limit order book contains any information about impending changes to mid market. Using a data set consisting of all the changes of the limit order book throughout the trading day, a change point detection algorithm is used to detect what is deemed to be significant changes of the shape of the limit order book. A measurement of the limit order imbalance is constructed as a proxy for the shape of the order book which then is used as input signal to the change point detection algorithm. A new data set is created based on the detected change points and a regression is run based on these to forecast price changes. It is found that the change point data set contains a certain amount of information about impending price changes

Data analysis in deep learning classification models, a financial application for bitcoin

Uses for machine learning methods have dramatically increased over the last decade. With a diverse array of industries making use of it, it is no surprise that the financial industry has been one of its first adopters and pioneer in its development. However, precise measurements must be considered when dealing with financial data extracted from the market. This work project is an execution of Professor Marcos López de Prado (Cornell University)data analysis techniques for financial machine learning algorithms. The prepared data was then used as an input in a deep neural network for multi class classification, with the objective of making price direction predictions. Bitcoin was the selected financial instrument for this study, given its high volatility and its virtually global accessibility

The Use of Control Charts in the Study of Bitcoin’s Price Variability

The focus of this research is bitcoin’s variability and its comparison with the variability of the EURO/USD exchange rate. Virtual currencies have been evolving in a dynamic way in the last few years. Under 600 different virtual currencies, the most successful was bitcoin. Its adherents saw in it an alternative to the traditional means of payments allowing the performance of real-time transactions at low costs. The accessibility, where no financial infrastructure is ensured or where either limited or no international agreements exist between financial and banking institutions was also an advantage. The opponents perceived this as a temporary curiosity with no future. Time confirmed that bitcoin has gained on popularity and the exchange rate to the main currencies rose in a dynamic way. The analysts, however, underline that the bitcoin is too volatile and unpredictable, so it cannot compete against the main currencies. The aim of this research is to compare the bitcoin (BTC) to US Dollar (USD) exchange rate and Euro to USD exchange rate volatility using control charts. The results have shown that BTC/USD exchange rate volatility is strongly affected by unexpected price jumps during the period (2010–2016), an act that significantly distinguishes it from more stable and predictable EUR/USD exchange rate variability

The comovement of the selective ASEAN stock markets: is there any impact on Malaysian stock market?

This paper investigates the cointegration relationship in the monthly returns among five stock market indices of ASEAN countries namely FTSE Bursa Malaysia KLCI, Bangkok Stock Exchange of Thailand, Ho Chi Minh Stock Exchange, Jakarta Composite Index and Philippines Stock Exchange. The period of study is between January 2001 and December 2015. The Johansen-Juselius cointegration test and Vector Error Correction Model (VECM) are applied to examine the cointegration between Malaysian stock market index with the other four selected stock market indices. Findings indicate that there is cointegration relationship among the five selected ASEAN stock market indices. The VECM long run results show that the Bangkok Stock Exchange of Thailand has the highest influence on the FTSE Bursa Malaysia KLC

Distribution-free cumulative sum control charts using bootstrap-based control limits

This paper deals with phase II, univariate, statistical process control when a set of in-control data is available, and when both the in-control and out-of-control distributions of the process are unknown. Existing process control techniques typically require substantial knowledge about the in-control and out-of-control distributions of the process, which is often difficult to obtain in practice. We propose (a) using a sequence of control limits for the cumulative sum (CUSUM) control charts, where the control limits are determined by the conditional distribution of the CUSUM statistic given the last time it was zero, and (b) estimating the control limits by bootstrap. Traditionally, the CUSUM control chart uses a single control limit, which is obtained under the assumption that the in-control and out-of-control distributions of the process are Normal. When the normality assumption is not valid, which is often true in applications, the actual in-control average run length, defined to be the expected time duration before the control chart signals a process change, is quite different from the nominal in-control average run length. This limitation is mostly eliminated in the proposed procedure, which is distribution-free and robust against different choices of the in-control and out-of-control distributions.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS197 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Factors affecting development patterns: econometric investigation of the Japan equity market

In this paper it is assumed that equity markets reflect the development of the overall economy of a country. Equity markets, among other factors, are considerably affected by factors such as inflation or deflation. Therefore, when inflationary or deflationary pressures appear, Central Banks try to manage those pressures in order to minimise their impact on the economy. In this paper, the case of Japan will be examined. Japan can be considered an example of a country which was under extended deflationary pressures for about three decades. In this study, the authors investigate different time frames for the Japan equity market. The research is based on Japan equity market (NIKKEI) returns. The authors aim to answer the question of whether the Japanese market complies with the Efficient Market Hypothesis (EMH) for different time frames, as well as test analytically if Japan’s stock market and economy have improved after the implementation of different attempts at Quantitative Easing (QEs), a Zero Interest Rate Policy (ZIRP) or a Negative Interest Rate Policy (NIRP) to curb deflationary impacts on the economy. The analysis and obtained results could be useful for risk and portfolio management, and could be extended to other markets

Do Google Trends and Shariah Compliant Stocks Co-Integrated? An Evidence from India

The objective of the study is to understand the dynamic relationship between Shariah-compliant stocks and the Google search value index (GSVI). The search strength is identified by the search volume of Shariah-compliant stocks on Google. The sample for the study consists of Shariah-compliant stocks commonly available in all the three Shariah indices in India, sample stock data has been extracted on a weekly basis from Sept 2014 to Sept 2019. The results of the study are based on the diagnostic analysis suggests that there is no serial correlation as demonstrated by LM residual test, CUSUM test shows stability in data, coefficient Wald test is showing there is no short-run causality running between selected Shariah-compliant stocks and GSVI. The outcome suggests that there is a long-run equilibrium relationship existing between Shariah-compliant stocks and the Google search value index. Trace statistics has five co-integration equations and Max-Eigen statistics has one co-integration. The vector error correction model (VECM) suggests the acceptability of the model. There are many potential investment opportunities for investors in the Islamic stock market of India. The motive of Shariah is to provide an avenue for ethical and viable investment to the investors. This study will not only be advantageous for the Muslim investors but also the other investors, industrialist, Shariah-compliant advisor as well.

Real-Time Detection of Local No-Arbitrage Violations

This paper focuses on the task of detecting local episodes involving violation of the standard It\^o semimartingale assumption for financial asset prices in real time that might induce arbitrage opportunities. Our proposed detectors, defined as stopping rules, are applied sequentially to continually incoming high-frequency data. We show that they are asymptotically exponentially distributed in the absence of Ito semimartingale violations. On the other hand, when a violation occurs, we can achieve immediate detection under infill asymptotics. A Monte Carlo study demonstrates that the asymptotic results provide a good approximation to the finite-sample behavior of the sequential detectors. An empirical application to S&P 500 index futures data corroborates the effectiveness of our detectors in swiftly identifying the emergence of an extreme return persistence episode in real time