A Survey of Forex and Stock Price Prediction Using Deep Learning

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising

Identification des régimes et regroupement des séquences pour la prévision des marchés financiers

Abstract : Regime switching analysis is extensively advocated to capture complex behaviors underlying financial time series for market prediction. Two main disadvantages in current approaches of regime identification are raised in the literature: 1) the lack of a mechanism for identifying regimes dynamically, restricting them to switching among a fixed set of regimes with a static transition probability matrix; 2) failure to utilize cross-sectional regime dependencies among time series, since not all the time series are synchronized to the same regime. As the numerical time series can be symbolized into categorical sequences, a third issue raises: 3) the lack of a meaningful and effective measure of the similarity between chronological dependent categorical values, in order to identify sequence clusters that could serve as regimes for market forecasting. In this thesis, we propose a dynamic regime identification model that can identify regimes dynamically with a time-varying transition probability, to address the first issue. For the second issue, we propose a cluster-based regime identification model to account for the cross-sectional regime dependencies underlying financial time series for market forecasting. For the last issue, we develop a dynamic order Markov model, making use of information underlying frequent consecutive patterns and sparse patterns, to identify the clusters that could serve as regimes identified on categorized financial time series. Experiments on synthetic and real-world datasets show that our two regime models show good performance on both regime identification and forecasting, while our dynamic order Markov clustering model also demonstrates good performance on identifying clusters from categorical sequences.L'analyse de changement de régime est largement préconisée pour capturer les comportements complexes sous-jacents aux séries chronologiques financières pour la prédiction du marché. Deux principaux problèmes des approches actuelles d'identifica-tion de régime sont soulevés dans la littérature. Il s’agit de: 1) l'absence d'un mécanisme d'identification dynamique des régimes. Ceci limite la commutation entre un ensemble fixe de régimes avec une matrice de probabilité de transition statique; 2) l’incapacité à utiliser les dépendances transversales des régimes entre les séries chronologiques, car toutes les séries chronologiques ne sont pas synchronisées sur le même régime. Étant donné que les séries temporelles numériques peuvent être symbolisées en séquences catégorielles, un troisième problème se pose: 3) l'absence d'une mesure significative et efficace de la similarité entre les séries chronologiques dépendant des valeurs catégorielles pour identifier les clusters de séquences qui pourraient servir de régimes de prévision du marché. Dans cette thèse, nous proposons un modèle d'identification de régime dynamique qui identifie dynamiquement des régimes avec une probabilité de transition variable dans le temps afin de répondre au premier problème. Ensuite, pour adresser le deuxième problème, nous proposons un modèle d'identification de régime basé sur les clusters. Notre modèle considère les dépendances transversales des régimes sous-jacents aux séries chronologiques financières avant d’effectuer la prévision du marché. Pour terminer, nous abordons le troisième problème en développant un modèle de Markov d'ordre dynamique, en utilisant les informations sous-jacentes aux motifs consécutifs fréquents et aux motifs clairsemés, pour identifier les clusters qui peuvent servir de régimes identifiés sur des séries chronologiques financières catégorisées. Nous avons mené des expériences sur des ensembles de données synthétiques et du monde réel. Nous démontrons que nos deux modèles de régime présentent de bonnes performances à la fois en termes d'identification et de prévision de régime, et notre modèle de clustering de Markov d'ordre dynamique produit également de bonnes performances dans l'identification de clusters à partir de séquences catégorielles

Hedging US stock markets in wake of COVID-19 pandemic

The purpose of this pro gradu -study is to examine widely regarded hedging assets against S&P 500 stock index during the COVID-19 pandemic. The motivation to study these assets relies on previous literature and unique market conditions of COVID-19 pandemic for markets. Flight-to-quality is often observed during crisis periods when there is turmoil and distress in financial markets. Increased uncertainty drives investors to become more risk-averse and allocate capital in more stable asset classes. Previous literature has indicated that commodities, government bonds and bitcoin could benefit from such phenomenon. Additionally, by pre-emptively alloca-ting portfolio capital to such assets investors could possibly effectively hedge potential losses of one asset with gains on another assets. This study follows methodology introduced by Baur and McDermott (2010) to compare different assets hedging and safe-haven performance during the COVID-19 markets. Such retrospective analysis provides effective tool for this thesis to provide insight to support future investing theses during market uncertainty. The focus is to set on the United States as the largest open markets in the world with data running from 1st of January 2020 till 20th of December 2021. The data is first cleaned to represent same trading days as the New York Stock Exchange trading days, after which daily returns are presented in log-format of which bottom 1%, 5% and 10% quantiles are picked with dummy variables. Identical formulas are used for different assets to determinate the effectiveness to limit the volatility during these trading days in order to find out possible safe haven effectiveness and hedging ability. The obtained results suggest that most of the assets failed to act as safe haven asset during the COVID-19 markets. Only bonds successfully hedged stock market volatility and losses for inves-tors. When compared with previous literature this study does affirm and contradict number of previous studies. These results can be affected by number of factors such as different sample periods and methodological choices. Results do however indicate that U.S. Government bonds with different maturities did act as hedge against S&P 500 stock market index during the sample period. In addition, gold can be regarded as an effective diversifier with S&P 500 stock index

Research in Emerging Financial Technologies

In chapter one we investigate the price clustering of non-fiat cryptocurrency exchange rates or the pricing of items in cryptocurrency such as bitcoin, which has been accepted as payment at a growing list of companies. For litecoin, a non-fiat currency, priced in terms of satoshi, one hundred millionth of a bitcoin, over 35% are priced at 100 satoshi increments, providing support for the negotiation hypothesis. There is also strategic pricing at 1 satoshi below or above the 100 satoshi increments. At the transaction level, we find that prices are mainly formed due to negotiations and strategic trading, instead of based on psychologically appealing numbers in the order of 0, 5, and others.In the second chapter we examine commonality in returns and liquidity (trading volume) for Bitcoin-fiat currency pairs, each trading on an exchange in a country with a single time zone. We find evidence that one common factor explains about 54% of the variance in hourly trading volume. We find strong support for the presence of a microstructure-noise volatility multiplier. Volume is higher on local exchanges during local working hours, reflecting a pattern also seen in forex markets, and supporting the view that trading patterns depend on the location of trade rather than the location of the asset being traded.In the final chapter we use the distribution from Benfords Law to investigate whether fake volume is reported for five bitcoin exchanges that are either regulated by the US Department of Treasury or have licenses from the State of New York and three exchanges that are not so regulated. Using counts of first digits, counts of second digits, and sums of numbers beginning with the same first two digits, we find that the distribution of minute-level volume of regulated exchanges deviate less from Benfords expected distribution than the remaining three exchanges. We find that the proportion of first digits deviate less for the Bitstamp, Coinbase, and ItBit exchanges, justifying their use as the basis for the index price for CME Bitcoin Futures contracts (BTCA)

Contemporary Research on Business and Management

This book contains selected papers presented at the 4th International Seminar of Contemporary Research on Business and Management (ISCRBM 2020), which was organized by the Alliance of Indonesian Master of Management Program (APMMI) and held in Surubaya, Indonesia, 25-27 November 2020. It was hosted by the Master of Management Program Indonesia University and co-hosts Airlangga University, Sriwijaya University, Trunojoyo University of Madura, and Telkom University, and supported by Telkom Indonesia and Triputra. The seminar aimed to provide a forum for leading scholars, academics, researchers, and practitioners in business and management area to reflect on current issues, challenges and opportunities, and to share the latest innovative research and best practice. This seminar brought together participants to exchange ideas on the future development of management disciplines: human resources, marketing, operations, finance, strategic management and entrepreneurship

Tendencias líderes de investigación sobre estrategias de trading

[EN] Trading strategies have attracted the attention of academic researchers and practitioners for a long time, but most specially in recent years due to the explosion of high-quality databases and computation capacity. Numerous studies are devoted to the analysis and proposal of trading strategies which cover aspects such as trend prediction, variables selection, technical analysis, pattern recognition etc. and apply many di erent methodologies. This paper conducts a meta-literature review which covers 1187 research articles from 1984 to 2020. The aim of this paper is to show the increasing importance of the topic and present a systematic study of the leading research areas, countries, institutions and authors contributing to this field. Moreover, a network analysis to identify the main research streams and future research opportunities is conducted.[ES] La creación de estrategias de inversión siempre ha atraído la atención de los académicos y de los inversores profesionales, pero, indudablemente, esta popularidad ha aumentado en los últimos años, con la aparición de bases de datos más completas y mayor potencia de cálculo de las computadoras. Son numerosos los estudios que analizan y proponen estrategias de inversión y que tratan aspectos como la predicción de la tendencia, la selección de variables, el análisis técnico, el reconocimiento de patrones etc. aplicando diferentes metodologías. En este trabajo se realiza un estudio bibliográfico que abarca 1187 artículos de investigación desde 1984 hasta 2020. El objetivo es mostrar la creciente importancia de este campo de investigación y presentar un análisis sistemático de los países, instituciones y autores que más están contribuyendo al avance del conocimiento. Además, se realiza un análisis de redes para identificar las principales áreas de investigación y las tendencias futuras.Oliver-Muncharaz, J.; García García, F. (2020). Leading research trends on trading strategies. Finance, Markets and Valuation. 6(2):27-54. https://doi.org/10.46503/LHTP1113S27546

Investor Sentiment and Fund Market Anomalies: Evidence from Closed-end Fund, Exchange-traded Fund and Real Estate Investment Trust

The investor sentiment hypothesis has become a promising avenue by way of a behavioural approach to complementing conventional explanations of financial market anomalies. In response to the problems exhibited in the existing theories, the investor sentiment hypothesis has been widely tested and the results of which turn out to be able to successfully explain the market anomalies to a great extent. The thesis applies the investor sentiment theory to analysing the fund anomalies in both the UK and US markets. The test results and their interpretations may help promote a better understanding of the investor sentiment and its impacts including their geographical differences. We contribute to the literature by focusing on the sentiment measures, among others. Since the investor sentiment reflects the investors’ behaviour and psychology, it is hard to be properly captured. We have constructed the proxies for the sentiment factor in both direct and indirect forms. The first fund anomaly we analysed is the “closed-end fund puzzle”. The puzzle is so-called because at IPO, the fund is issued at a premium to the net asset value (NAV); however, thispremium disappears in the next few months. The fund then trades at a discount. This discount is not fixed, varying substantially during the closure period. When the closed-end fund is either converted into an open-end fund or liquidated, the discount shrinks and the share price will rise. We construct an out-of-sample test by using the two-factor and five-factor models. The results show that the investor sentiment can contribute to explaining closed-end fund discounts in the UK market and it is more prevalent in smaller size portfolios. We also find the evidence to support investor sentiment as an important factor to represent systematic risk in the return generating process. Next, we examine the price deviations of Exchange Traded Funds (ETFs). Unlike closed end funds whose prices also deviate from the NAV, ETFs, through a mechanism known as redemption in-kind, allow institutional investors to potentially earn a profit by arbitraging away these price deviations through creating and deleting outstanding shares of the ETF. Hence, we are motivated to identify the factors that may impact on the determination of these premiums and discounts to the NAV. We first construct a sentiment proxy from the derivative market variables such as the option put–call trading volume ratio and the open interest ratio. Then we develop a sentiment proxy based on the consumer confidence index, obtained from the mainstream consumer surveys and this proxy is taken to the individual fund level. The results provide evidence that this sentiment proxy has explanatory power for most individual ETF mispricing. We take the whole industry into account and find that the sentiment factor has incremental explanatory power and is positively related to the fund premium. The evidence also shows that more sentiment-sensitive ETFs are those that have smaller, younger and volatile stocks with low dividend yields. Finally, the thesis considers the fund anomaly in the form of the REIT price momentum. In order to investigate the momentum profitability, we classify the formation period into two sentiment states, i.e. the optimistic and pessimistic periods. Evidence indicates that when sentiment is high, the REIT momentum profitability is substantial and significant; however, when the sentiment is low, the profits from the REIT momentum are much lower and not significant. We also examine the interplay between REIT liquidity and momentum profitability. We find that high REIT liquidity portfolios generate higher momentum returns, but this is only significant when the sentiment is optimistic. Furthermore, consistent with our previous findings, our evidence that momentum is generally larger for smaller companies confirms that the size effect is still available in the REIT industry. This is because the smaller companies are often difficult to value, as they are more prone to subjective evaluations. The sentiment thus could be more significant in small size companies

Winners and Losers of the 2017 Tax Cut & Jobs Act

Using data from 720 publicly traded companies, this paper looks at the impact of the 2017 Tax Cut and Jobs Act through two different studies. Firstly, the paper finds that, on average, there was a reduction in the GAAP effective tax rate (ETR) of 8.88%. However, the first study identifies key firm characteristics that determine the actual observed change in ETR for any given firm. The biggest determinant of the change in ETR is a firm’s prior ETR, with percentage foreign income, R&D expense, and firm size as other significant predictors. The second study seeks to identify whether the tax change had any impact on firms’ investment behavior, one of the noted reasons for the policy change. The results did not show that the TCJA, reduction in ETR, had any impact on firm investment behavior as measured by capital expenditures and R&D spending