Algorithms

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2 pages, 129 KiB

Open AccessEditorial

Special Issue on Algorithms in Computational Finance

by V L Raju Chinthalapati and Edward Tsang

Algorithms 2019, 12(4), 69; https://doi.org/10.3390/a12040069 - 31 Mar 2019

Cited by 2 | Viewed by 4241

Abstract

Algorithms play an important part in finance [...] Full article

(This article belongs to the Special Issue Algorithms in Computational Finance)

Research

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17 pages, 2318 KiB

Open AccessArticle

The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach

by Vasilios Plakandaras, Periklis Gogas and Theophilos Papadimitriou

Algorithms 2019, 12(1), 1; https://doi.org/10.3390/a12010001 - 20 Dec 2018

Cited by 20 | Viewed by 7057

Abstract

An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated [...] Read more.

An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated form is the risk stemming from geopolitical events, such as wars, political tensions, and conflicts. In contrast, the effects of terrorist acts have been thoroughly examined in the relevant literature. In this paper, we examine the potential ability of geopolitical risk of 14 emerging countries to forecast several assets: oil prices, exchange rates, national stock indices, and the price of gold. In doing so, we build forecasting models that are based on machine learning techniques and evaluate the associated out-of-sample forecasting error in various horizons from one to twenty-four months ahead. Our empirical findings suggest that geopolitical events in emerging countries are of little importance to the global economy, since their effect on the assets examined is mainly transitory and only of regional importance. In contrast, gold prices seem to be affected by fluctuation in geopolitical risk. This finding may be justified by the nature of investments in gold, in that they are typically used by economic agents to hedge risk. Full article

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14 pages, 833 KiB

Open AccessArticle

Classification of Normal and Abnormal Regimes in Financial Markets

by Jun Chen and Edward P. K. Tsang

Algorithms 2018, 11(12), 202; https://doi.org/10.3390/a11120202 - 12 Dec 2018

Cited by 7 | Viewed by 5683

Abstract

When financial market conditions change, traders adopt different strategies. The traders’ collective behaviour may cause significant changes in the statistical properties of price movements. When this happens, the market is said to have gone through “regime changes”. The purpose of this paper is [...] Read more.

When financial market conditions change, traders adopt different strategies. The traders’ collective behaviour may cause significant changes in the statistical properties of price movements. When this happens, the market is said to have gone through “regime changes”. The purpose of this paper is to characterise what is a “normal market regime” as well as what is an “abnormal market regime”, under observations in Directional Changes (DC). Our study starts with historical data from 10 financial markets. For each market, we focus on a period of time in which significant events could have triggered regime changes. The observations of regime changes in these markets are then positioned in a designed two-dimensional indicator space based on DC. Our results suggest that the normal regimes from different markets share similar statistical characteristics. In other words, with our observations, it is possible to distinguish normal regimes from abnormal regimes. This is significant, because, for the first time, we can tell whether a market is in a normal regime by observing the DC indicators in the market. This opens the door for future work to be able to dynamically monitor the market for regime change. Full article

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13 pages, 306 KiB

Open AccessArticle

An Algorithmic Look at Financial Volatility

by Lin Ma and Jean-Paul Delahaye

Algorithms 2018, 11(11), 185; https://doi.org/10.3390/a11110185 - 13 Nov 2018

Cited by 3 | Viewed by 3440

Abstract

In this paper, we attempt to give an algorithmic explanation to volatility clustering, one of the most exploited stylized facts in finance. Our analysis with daily data from five exchanges shows that financial volatilities follow Levin’s universal distribution Kirchherr et al. (1997) once [...] Read more.

In this paper, we attempt to give an algorithmic explanation to volatility clustering, one of the most exploited stylized facts in finance. Our analysis with daily data from five exchanges shows that financial volatilities follow Levin’s universal distribution Kirchherr et al. (1997) once transformed into equally proportional binary strings. Frequency ranking of binary trading weeks coincides with that of their Kolmogorov complexity estimated by Delahaye et al. (2012). According to Levin’s universal distribution, large (resp. small) volatilities are more likely to be followed by large (resp. small) ones since simple trading weeks such as “00000” or “11111” are much more frequently observed than complex ones such as “10100” or “01011”. Thus, volatility clusters may not be attributed to behavioral or micro-structural assumptions but to the complexity discrepancy between finite strings. This property of financial data could be at the origin of volatility autocorrelation, though autocorrelated volatilities simulated from Generalized Auto-Regressive Conditional Heteroskedacity (hereafter GARCH) cannot be transformed into universally distributed binary weeks. Full article

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29 pages, 8615 KiB

Open AccessArticle

Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework

by Amer Bakhach, Venkata L. Raju Chinthalapati, Edward P. K. Tsang and Abdul Rahman El Sayed

Algorithms 2018, 11(11), 171; https://doi.org/10.3390/a11110171 - 28 Oct 2018

Cited by 13 | Viewed by 5221

Abstract

The Directional Changes (DC) framework is an approach to summarize price movement in financial time series. Some studies have tried to develop trading strategies based on the DC framework. Dynamic Backlash Agent (DBA) is a trading strategy that has been developed based on [...] Read more.

The Directional Changes (DC) framework is an approach to summarize price movement in financial time series. Some studies have tried to develop trading strategies based on the DC framework. Dynamic Backlash Agent (DBA) is a trading strategy that has been developed based on the DC framework. Despite the promising results of DBA, DBA employed neither an order size management nor risk management components. In this paper, we present an improved version of DBA named Intelligent DBA (IDBA). IDBA overcomes the weaknesses of DBA as it embraces an original order size management and risk management modules. We examine the performance of IDBA in the forex market. The results suggest that IDBA can provide significantly greater returns than DBA. The results also show that the IDBA outperforms another DC-based trading strategy and that it can generate annualized returns of about 30% after deducting the bid and ask spread (but not the transaction costs). Full article

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16 pages, 3375 KiB

Open AccessArticle

A Machine Learning View on Momentum and Reversal Trading

by Zhixi Li and Vincent Tam

Algorithms 2018, 11(11), 170; https://doi.org/10.3390/a11110170 - 26 Oct 2018

Cited by 9 | Viewed by 8263

Abstract

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it [...] Read more.

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns. Full article

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38 pages, 2218 KiB

Open AccessArticle

Chronotype, Risk and Time Preferences, and Financial Behaviour

by Di Wang, Frank McGroarty and Eng-Tuck Cheah

Algorithms 2018, 11(10), 153; https://doi.org/10.3390/a11100153 - 10 Oct 2018

Cited by 3 | Viewed by 4295

Abstract

This paper examines the effect of chronotype on the delinquent credit card payments and stock market participation through preference channels. Using an online survey of 455 individuals who have been working for 3 to 8 years in companies in mainland China, the results [...] Read more.

This paper examines the effect of chronotype on the delinquent credit card payments and stock market participation through preference channels. Using an online survey of 455 individuals who have been working for 3 to 8 years in companies in mainland China, the results reveal that morningness is negatively associated with delinquent credit card payments. Morningness also indirectly predicts delinquent credit card payments through time preference, but this relationship only exists when individuals’ monthly income is at a low and average level. On the other hand, financial risk preference accounts for the effect of morningness on stock market participation. Consequently, an additional finding is that morningness is positively associated with financial risk preference, which contradicts previous findings in the literature. Finally, based on the empirical evidence, we discuss the plausible mechanisms that may drive these relationships and the implications for theory and practice. The current study contributes to the literature by examining the links between circadian typology and particular financial behaviour of experienced workers. Full article

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19 pages, 642 KiB

Open AccessArticle

Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction

by Sanjiv R. Das, Karthik Mokashi and Robbie Culkin

Algorithms 2018, 11(9), 138; https://doi.org/10.3390/a11090138 - 13 Sep 2018

Cited by 13 | Viewed by 4247

Abstract

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly [...] Read more.

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index, but not strongly enough to reject market efficiency. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P 500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests. Full article

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19 pages, 417 KiB

Open AccessArticle

Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization

by Yixuan Ren, Tao Ye, Mengxing Huang and Siling Feng

Algorithms 2018, 11(5), 72; https://doi.org/10.3390/a11050072 - 15 May 2018

Cited by 15 | Viewed by 8264

Abstract

In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does [...] Read more.

In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does not conform to the investment environment under realistic conditions. Therefore, we added a series of constraints into basic portfolio optimization model, which reflect the realistic investment environment, such as skewness and kurtosis. In addition, we consider two kinds of risk measures: conditional value at risk and value at risk. Most important of all, in this paper, we introduce Gray Wolf Optimization (GWO) algorithm into portfolio optimization model, which simulates the gray wolf’s social hierarchy and predatory behavior. In the numerical experiments, we compare the GWO algorithm with Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). The experimental results show that GWO algorithm not only shows better optimization ability and optimization efficiency, but also the portfolio optimized by GWO algorithm has a better performance than FTSE100 index, which prove that GWO algorithm has a great potential in portfolio optimization. Full article

(This article belongs to the Special Issue Algorithms in Computational Finance)

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