•A novel trading strategy based on the event-based concept of directional changes.•The trading strategy includes classification and regression algorithms.•Algorithm tested on 1000 datasets from 20 FX ...currency pairs.•Proposed approach is able to generate new and profitable trading strategies.•Proposed approach significantly outperforms all other benchmarks.
Most forecasting algorithms in financial markets use physical time for studying price movements, making the flow of time discontinuous. The use of physical time scale can make traders oblivious to significant activities in the market, which poses a risk. Directional changes (DC) is an alternative approach that uses event-based time to sample data. In this work, we propose a novel DC-based framework, which uses machine learning algorithms to predict when a trend will reverse. This allows traders to be in a position to take an action before this happens and thus increase their profitability. We combine our approach with a novel DC-based trading strategy and perform an in-depth investigation, by applying it to 10-min data from 20 foreign exchange markets over a 10-month period. The total number of tested datasets is 1,000, which allows us to argue that our results can be generalised and are widely applicable. We compare our results to ten benchmarks (both DC and non-DC based, such as technical analysis and buy-and-hold). Our findings show that our proposed approach is able to return a significantly higher profit, as well as reduced risk, and statistically outperform the other trading strategies in a number of different performance metrics.
Recently the depth and extreme consequences of coronavirus in Nigeria have been alarming. In the last few months, the government has undertaken various steps by introducing certain policies that ...would help in reducing, if not eradicating the socio-economic implications of this novel virus outbreak in Nigeria. The study examines the implications of COVID 19 on the economic status of Nigeria using a daily time series data analysis for eighty-one days during the first heavy lockdown (27th February 2020 to 17th May 2020). The data used for the study were extracted from secondary sources: the NCDC reports, CBN reports, Nigeria Stock Exchange reports, Accuweather reports, and Bloomberg within the period of the study. The data was analyzed using the ordinary least square regression incorporating the correlation test. The study found that daily COVID 19 confirmed cases and daily FOREX rates have a negative and significant impact on daily oil price, a mirror of daily output growth whereas daily share index has a positive impact. Only the daily temperature report was insignificant but positive in the model. The study recommended that Nigerians should strongly adhere to the preventive guidelines as stipulated by the Nigeria Centre for Disease Control (NCDC) in line with World Health Organisation (WHO) recommendations.
•A combined computational intelligence technique for trend classification and trading in Forex markets.•An Ensemble multi-class SVM for efficient trend forecasting into uptrend, sideway, and ...downtrend.•A fuzzy-based trading system comprising multiple AND-OR Buy/Sell fuzzy rules.•Utilizing NSGA-II to optimize the hyperparameters of the fuzzy trading system.
Foreign exchange (Forex) market is the biggest currency exchange market in the world. Existing trading systems in Forex markets based on technical analysis use crisp technical indicators to provide Buy/Sell signals to the trader, only when the indicator value crosses a given threshold level. This strict and noise-sensitive condition can be replaced through uncertainty handling of indicators using fuzzy numbers to generate Buy/Sell signals with fuzzy memberships functions. To achieve this purpose, this paper presents a combined technique based on ensemble multi-class support vector machine (EmcSVM) and fuzzy NSGA-II for efficient trend classification and trading in Forex markets. At first, EmcSVM is used to forecast and classify the future market trend into uptrend, sideway, and downtrend. Then, NSGA-II is applied to optimize the hyperparameters of the proposed fuzzy trading system comprising multiple AND-OR Buy/Sell technical rules for uptrend/downtrend markets. The hyperparameters include indicator selection within each rule, importance weights of the different rules, and final decision thresholds for Buy/Sell models, while the objective is to maximize average return on investment (ROI) and minimize average draw-down of all transactions. The proposed method has been successfully developed and tested on real data from the Forex market for EUR/USD currency pair in a 6-year timeframe from 2014 to 2019. Obtained results show that the proposed method outperforms the existing crisp trading systems, with 80.8% precision, 72.4% recall, 94.1% annual ROI, and 0.58% draw down.
•FOREX prediction through text mining of news is viable and effective.•Feature-selection by abstraction of word-hypernyms increases prediction accuracy.•Feature-weighting based on the sum of pos and ...neg sentiment scores is effective.•Feature-reduction based on maximum optimization for prediction-target is crucial.
In this paper a novel approach is proposed to predict intraday directional-movements of a currency-pair in the foreign exchange market based on the text of breaking financial news-headlines. The motivation behind this work is twofold: First, although market-prediction through text-mining is shown to be a promising area of work in the literature, the text-mining approaches utilized in it at this stage are not much beyond basic ones as it is still an emerging field. This work is an effort to put more emphasis on the text-mining methods and tackle some specific aspects thereof that are weak in previous works, namely: the problem of high dimensionality as well as the problem of ignoring sentiment and semantics in dealing with textual language. This research assumes that addressing these aspects of text-mining have an impact on the quality of the achieved results. The proposed system proves this assumption to be right. The second part of the motivation is to research a specific market, namely, the foreign exchange market, which seems not to have been researched in the previous works based on predictive text-mining. Therefore, results of this work also successfully demonstrate a predictive relationship between this specific market-type and the textual data of news. Besides the above two main components of the motivation, there are other specific aspects that make the setup of the proposed system and the conducted experiment unique, for example, the use of news article-headlines only and not news article-bodies, which enables usage of short pieces of text rather than long ones; or the use of general financial breaking news without any further filtration.
In order to accomplish the above, this work produces a multi-layer algorithm that tackles each of the mentioned aspects of the text-mining problem at a designated layer. The first layer is termed the Semantic Abstraction Layer and addresses the problem of co-reference in text mining that is contributing to sparsity. Co-reference occurs when two or more words in a text corpus refer to the same concept. This work produces a custom approach by the name of Heuristic-Hypernyms Feature-Selection which creates a way to recognize words with the same parent-word to be regarded as one entity. As a result, prediction accuracy increases significantly at this layer which is attributed to appropriate noise-reduction from the feature-space.
The second layer is termed Sentiment Integration Layer, which integrates sentiment analysis capability into the algorithm by proposing a sentiment weight by the name of SumScore that reflects investors’ sentiment. Additionally, this layer reduces the dimensions by eliminating those that are of zero value in terms of sentiment and thereby improves prediction accuracy.
The third layer encompasses a dynamic model creation algorithm, termed Synchronous Targeted Feature Reduction (STFR). It is suitable for the challenge at hand whereby the mining of a stream of text is concerned. It updates the models with the most recent information available and, more importantly, it ensures that the dimensions are reduced to the absolute minimum.
The algorithm and each of its layers are extensively evaluated using real market data and news content across multiple years and have proven to be solid and superior to any other comparable solution. The proposed techniques implemented in the system, result in significantly high directional-accuracies of up to 83.33%.
On top of a well-rounded multifaceted algorithm, this work contributes a much needed research framework for this context with a test-bed of data that must make future research endeavors more convenient. The produced algorithm is scalable and its modular design allows improvement in each of its layers in future research. This paper provides ample details to reproduce the entire system and the conducted experiments.
The majority of forecasting methods use a physical time scale for studying price fluctuations of financial markets. Using physical time scales can make companies oblivious to significant activities ...in the market as the flow of time is discontinuous, which could translate to missed profitable opportunities or risk exposure. Directional changes (DC) has gained attention in the recent years by translating physical time series to event‐based series. Under this framework, trend reversals can be predicted by using the length of events. Having this knowledge allows traders to take an action before such reversals happen and thus increase their profitability. In this paper, we investigate how classification algorithms can be incorporated in the process of predicting trend reversals to create DC‐based trading strategies. The effect of the proposed trend reversal estimation is measured on 20 foreign exchange markets over a 10‐month period in a total of 1000 data sets. We compare our results across 16 algorithms, both DC and non‐DC based, such as technical analysis and buy‐and‐hold. Our findings show that the introduction of classification leads to return higher profit and statistically outperform all other trading strategies.
The foreign exchange market (FX) is a market for converting the currency of one country into that of another country. Spot exchange rates movements are carefully observed every minute around the ...world. But governments, banks and multinational companies generally develop decision-making under other frequencies of time like days, weeks, months, quarters, or semesters. Interval time series (ITS) assign an interval of values at every period of time. For example, daily or monthly lows and highs values are key examples of ITS. Several forecasting methods have been developed for ITS. Neural networks have attracted research focused on FX forecasting. The Multi-Layer Perceptron (MLP) with one hidden layer is one of the best networks for forecasting crisp time series. For ITS, the iMLP (interval MLP) was proposed as an extension of the MLP. The number and type of inputs and the number of neurons in the hidden layer (15 is the usual number) are key parameters to rank different architectures of the network. We analyze these hyperparameters in the forecasting performance of the iMLP through the EUR/USD on a low–high daily basis, on different behaviors such as uptrend, downtrend, or sideways; on different accuracy measures, including coverage and efficiency rates, and incorporating other rates such as GBP/USD or AUD/USD. The election of 15 neurons is discarded. Moreover, we compare these iMLP networks with the interval random walk and results are quite promising. Finally, we conclude that in any context of FX, several iMLP networks should be considered which opens new research avenues.
We propose a general framework for measuring short and long term dynamics in asset classes based on the wavelet presentation of clustering analysis. The empirical results show strong evidence of ...instability of the financial system aftermath of the global financial crisis. Indeed, both short and long-term dynamics have significantly changed after the global financial crisis. This study provides an interesting insights complex structure of global financial and economic system.
•We examine short and long term dynamics in linkages between world major markets during and after financial crisis.•There is strong evidence of instability of the world economy system after international financial crisis.•New clusters came out after crisis.•Short and long term dynamics has significantly changed, and several relationships have been shattered after crisis.
•We examine co-movements, volatility spillovers, hedging costs on new EU FX markets.•Conditional correlations and spillovers are not stable in time.•Correlations reach negative values during ...turbulent periods, positive in calm periods.•The cross-currency spillovers increase during market distress.•Periods of economic crisis are characterized by higher hedging costs.
We analyze time-varying exchange rate co-movements, hedging ratios, and volatility spillovers on the new EU forex markets during 1999M1-2018M5. We document significant differences in the extent of currency comovements during various periods of market distress that are related to real economic and financial events. These imply favorable diversification benefits: the hedge-ratio calculations show all three currencies bring hedging benefits during crisis periods, but at different costs. During calm periods, most of the volatilities are due to each currency’s own history. During the distress periods, volatility spillovers among currencies increase substantially and the Hungarian currency assumes a leading role.
In this paper, the application of the intuitionistic fuzzy rule-base evidential reasoning (IFRBER) to the development of a new optimized automated trading system (ATS) for the Forex market is ...presented. The used IFRBER approach represents the intuitionistic fuzzy sets in the framework of the evidence theory that allows us to avoid the revealed drawbacks of the IFS operational laws and enhance the overall performance of the IFRBER approach. It is shown that the IFRBER approach extracts from an analyzed system considerably more of useful for the decision making information than the usual fuzzy rule-base evidential reasoning (FRBER). Then based on the IFRBER, a new approach to make the justified transaction buying and selling decisions was proposed. This approach was used to develop a new optimized ATS for the Forex market. It is shown that due to the ability of a new approach to use more of useful information that present implicitly in the problem formulation than the proposed earlier usual fuzzy rule-base evidential reasoning method, the developed ATS provides a considerably more profitable and comfortable (with a higher percent of winning trades and with low risks) trading than the earlier developed ATS.
•Intuitionistic fuzzy rule-base evidential reasoning for generating trading decisions.•Automated trading system for the currency exchange (Forex) market.•Two-loops approach to the trading system optimization.•Fuzzy technical analysis indicators and their optimization.•Multiple criteria fuzzy optimization of the entire trading system.
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