A system to predict the S&P 500 using a bio-inspired algorithm

The goal of this research was to develop an algorithmic system capable of predicting the directional trend of the S&P 500 financial index. The approach I have taken was inspired by the biology of the human retina. Extensive research has been published attempting to predict different financial markets using historical data, testing on an in-sample and trend basis with many employing sophisticated mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, I moved to an out-of-sample strategy and am able to predict tomorrow’s (t+1) directional trend of the S&P 500 at 55.1%. The key elements that underpin my bio-inspired out-of-sample system are: Identification of 51 financial market data (FMD) inputs, including other indices, currency pairs, swap rates, that affect the 500 component companies of the S&P 500. The use of an extensive historical data set, comprising the actual daily closing prices of the chosen 51 FMD inputs and S&P 500. The ability to compute this large data set in a time frame of less than 24 hours. The data set was fed into a linear regression algorithm to determine the predicted value of tomorrow’s (t+1) S&P 500 closing price. This process was initially carried out in MatLab which proved the concept of my approach, but (3) above was not met. In order to successfully meet the requirement of handling such a large data set to complete the prediction target on time, I decided to adopt a novel graphics processing unit (GPU) based computational architecture. Through extensive optimisation of my GPU engine, I was able to achieve a sufficient speed up of 150x to meet (3). In achieving my optimum directional trend of 55.1%, an extensive range of tests exploring a number of trade offs were carried out using an 8 year data set. The results I have obtained will form the basis of a commercial investment fund. It should be noted that my algorithm uses financial data of the past 60-days, and as such would not be able to predict rapid market changes such as a stock market crash

Decision-Making Amplification Under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems

Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these “intelligent” systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed. Through an empirical experiment in the financial services industry, this study investigated the effects of perceived behavioral similarity (similarity state) on the dependent variables of recommendation acceptance, decision performance and decision efficiency under varying conditions of uncertainty (volatility state). It is hypothesized in this study that behavioral similarity as a design element will positively influence the acceptance rate of machine recommendations by human users. The level of uncertainty in the decision context is expected to moderate this relationship. In addition, an increase in recommendation acceptance should positively influence both decision performance and decision efficiency. The quantitative exploration of behavioral similarity as a design element revealed a number of key findings. Most importantly, behavioral similarity was found to positively influence the acceptance rate of machine recommendations. However, uncertainty did not moderate the level of recommendation acceptance as expected. The experiment also revealed that behavioral similarity positively influenced decision performance during periods of elevated uncertainty. This relationship was moderated based on the level of uncertainty in the decision context. The investigation of decision efficiency also revealed a statistically significant result. However, the results for decision efficiency were in the opposite direction of the hypothesized relationship. Interestingly, decisions made with the behaviorally similar decision aid were less efficient, based on length of time to make a decision, compared to decisions made with the low-similarity decision aid. The results of decision efficiency were stable across both levels of uncertainty in the decision context

Improving VIX Futures Forecasts using Machine Learning Methods

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best market volatility forecast. Using VIX futures and options data along with other technical indicators, our analysis compares multiple forecasting models for estimating the 1-month VIX futures contract (UX1) both 3 and 5-days forward. This analysis finds that machine/deep learning methods of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) provide improved results over existing linear regression, principal components analysis (PCA) and ARIMA methods. Comparing estimated versus actual test data, both the RNN and LSTM methods show lower mean squared error (MSE), lower mean absolute error (MAE), higher explained variance, and higher correlation. Finally, an accuracy matrix was generated for each model, which showed RNN and LSTM had better overall accuracy due to high true positive and negative forecasts as well as much lower false positive forecasts

Transformer-based deep learning model for stock return forecasting : Empirical evidence from US markets in 2012–2021

A growing number of studies in recent years have deployed various machine learning methods for financial time series analysis. The ability of machine learning methods to deal with complex and nonlinear data sets, as well as the increasing amount of available data and computational capacity, has pushed research further in this direction. While machine learning methods are nowadays widely used for forecasting financial time series, the results have been mixed. The rapid increase in machine learning research has also meant that new and more advanced models are being developed all the time. In many areas where machine learning methods are employed, designs based on the Transformer deep learning model often represent the state-of-the-art. However, the applications of the Transformer model for financial tasks are still in their infancy as only a few studies have been published on the matter. This study aims to investigate the feasibility of a Transformer-based deep learning model for stock return prediction. The feasibility is tested by predicting the daily directional movements of four different US stock indices on an out-of-sample period from the start of 2012 until the end of 2021. Only historical price data is utilized to predict the directional returns with two sets of explanatory variables. The model performance is tested against benchmarks and evaluated using various performance criteria such as prediction accuracy. Moreover, a trading strategy is carried out to reveal possible profitable attributes of the Transformer-based model. The reported classification accuracy over the whole empirical sample for the better Transformer model is 52.52% while LSTM, another deep learning model used as a benchmark, achieves an accuracy of 53.87%. However, the Transformer model manages to defeat all the benchmark models in every other performance metric. When the performances are tested using the trading strategy, the best Transformer model is able to generate an annualized return of 15.7% before transaction costs. The best performing benchmark, a simple buy-and-hold strategy, yields a return of 14.2%. The two tested Transformer models also have the highest Sharpe ratios out of the tested models at 1.063 and 1.061. Nevertheless, after transaction costs are taken into account, none of the tested models beat a simple buy-and-hold strategy in terms of profitability. Although the Transformer model was not able to perform superiorly throughout the sample period, it nevertheless exhibited increased predictive performance over shorter periods. For example, the model seemed to exploit periods of higher volatility as seen during the start of the COVID-19 pandemic. Overall, although the predictive performance of the Transformer model in this study might leave more to be desired, the model undoubtedly has predictive properties which should encourage further research to be executed.Viime vuosina lisääntynyt määrä tutkimuksia on soveltanut koneoppimismenetelmiä rahoituksen aikasarja-analyyseissä. Koneoppimismenetelmien kyky käsitellä monimutkaisia ja epälineaarisia data-aineistoja, sekä lisääntynyt datan määrä ja laskentakapasiteetti ovat entisestään vauhdittaneet tutkimusta tällä alueella. Vaikka koneoppimismenetelmiä käytetään nykyisin laajalti rahoituksen aikasarjojen ennustamiseen, ovat niiden tuottamat tulokset olleet vaihtelevia. Koneoppimistutkimuksen nopea kasvu on myös tarkoittanut, että uusia ja kehittyneempiä malleja kehitetään kaiken aikaa. Monilla aloilla, joissa koneoppimista käytetään, alan johtavat mallit pohjautuvat usein Transformer-syväoppimismalliin. Transformer-pohjaisten mallien soveltaminen rahoituksen tehtäviin on kuitenkin vielä varhaisessa vaiheessa, sillä alalla on julkaistu vain muutamia tutkimuksia aiheesta. Tämä tutkielma pyrkii selvittämään Transformer-pohjaisen mallin soveltuvuutta osaketuottojen ennustamiseen. Soveltuvuutta testataan ennustamalla neljän eri yhdysvaltalaisen osakeindeksin päivittäisiä suunnanmuutoksia vuoden 2012 alusta vuoden 2021 loppuun. Tuottojen suunnan ennustamisessa hyödynnetään vain historiallista hintadataa kahdella joukolla muuttujia. Mallin suorituskykyä testataan ja verrataan muihin käytettyihin malleihin monin eri suorituskykymittarein, kuten esimerkiksi ennustustarkkuuden avulla. Lisäksi toteutetaan kaupankäyntistrategia, jotta nähtäisiin mallin tuottamien ennusteiden mahdollinen taloudellinen hyöty. Raportoitu ennustetarkkuus koko tutkimusotoksen ajalta oli paremmalla Transformer-mallilla 52,52%, kun sen sijaan vertailumallina käytetty LSTM-syväoppimismalli saavutti 53,87%:n ennustetarkkuuden. Kyseinen Transformer-malli onnistui kuitenkin suoriutumaan paremmin kuin vertailumallit kaikkien muiden suoritusmittareiden osalla. Kun mallien suoriutumista vertaillaan kaupankäyntistrategialla, paras Transformer-malli saavuttaa 15,7%:n vuosittaisen tuoton ennen kaupankäyntikustannuksia. Paras vertailukohta, yksinkertainen osta-ja-pidä-strategia tuottaa 14,2%:n tuoton. Kahdella testatulla Transformer-mallilla on myös korkeimmat Sharpen luvut: 1,063 ja 1,061. Kuitenkin, kun kaupankäyntikulut huomioidaan, yksikään testatuista malleista ei suoriudu osta-ja-pidä-strategiaa paremmin tuottojen osalta. Vaikka Transformer-malli ei pystynyt suoriutumaan selvästi parhaiten läpi koko tutkimusotoksen, se esitti kasvanutta suorituskykyä lyhempinä aikoina. Malli näytti pystyvän esimerkiksi hyödyntämään korkean volatiliteetin ajanjaksoja, kuten COVID-19-pandemian alkuaikaa. Kaiken kaikkiaan, vaikka Transformer-mallin ennustuskyky tässä tutkielmassa saattaa jättää toivomisen varaa, Transformer-malli on epäilemättä kykeneväinen ennustustehtävissä, minkä tulisi edistää lisätutkimusten tekemistä aiheesta

The macroeconomic factors affecting government bond yield in Indonesia, Malaysia, Thailand, and the Philippines

© The author(s) 2020. This publication is an open access article. © Benny Budiawan Tjandrasa, Hotlan Siagian, Ferry Jie, 2020 The government bond (GB) has become the most attractive investment portfolio option, even though many macroeconomic factors affect the bond yield. This paper aims to investigate the determining factor of local currency government bond yield by considering the inflation rate, credit default swap, stock market index, exchange rate, and volatility index. This study used 240 data panel from the Bloomberg stock market in the form of data panel covering Southeast developing countries, namely Indonesia, Thailand, Malaysia, and the Philippines, for five years or sixty months from January 2015 to December 2019. Data analysis used recursive models and multivariate regression techniques using EViews software. The random effect model results revealed that change in the foreign exchange rate and volatility indexes affected, partially and simultaneously, the changes in the stock market index. The result also showed that changes in the stock market index, inflation rate, and credit default swap affected, partially and simultaneously, government bond yield changes. These results suggest that the government bond yield could be managed by controlling volatility index, foreign exchange rate, stock market index, inflation rates, and credit default swaps. This finding could provide an insight into the policymaker and fiscal authority on managing the risk of government bonds under control during high volatility or even making it reasonably lower. This result could contribute to the current research in the field of financial management

Understanding the Relationship between Online Discussions and Bitcoin Return and Volume: Topic Modeling and Sentiment Analysis

This thesis examines Bitcoin related discussions on Bitcointalk.com over the 2013-2022 period. Using Latent Dirichlet Allocation (LDA) topic modeling algorithm, we discover eight distinct topics: Mining, Regulation, Investment/trading, Public perception, Bitcoin’s nature, Wallet, Payment, and Other. Importantly, we find differences in relations between different topics’ sentiment, disagreement (proxy for uncertainty) and hype (proxy for attention) on one hand and Bitcoin return and trading volume on the other hand. Specifically, among all topics, only the sentiment and disagreement of Investment/trading topic have significant contemporaneous relation with Bitcoin return. In addition, sentiment and disagreement of several topics, such as Mining and Wallet, show significant relationships with Bitcoin return only on the tails of the return distribution (bullish and bearish markets). In contrast, sentiment, disagreement, and hype of each topic show significant relation with Bitcoin volume across the entire distribution. In addition, whereas hype has a positive relation with trading volume in a low-volume market, this relation becomes negative in a high-volume market

Applications of Deep Learning Models in Financial Forecasting

In financial markets, deep learning techniques sparked a revolution, reshaping conventional approaches and amplifying predictive capabilities. This thesis explored the applications of deep learning models to unravel insights and methodologies aimed at advancing financial forecasting. The crux of the research problem lies in the applications of predictive models within financial domains, characterised by high volatility and uncertainty. This thesis investigated the application of advanced deep-learning methodologies in the context of financial forecasting, addressing the challenges posed by the dynamic nature of financial markets. These challenges were tackled by exploring a range of techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), autoencoders (AEs), and variational autoencoders (VAEs), along with approaches such as encoding financial time series into images. Through analysis, methodologies such as transfer learning, convolutional neural networks, long short-term memory networks, generative modelling, and image encoding of time series data were examined. These methodologies collectively offered a comprehensive toolkit for extracting meaningful insights from financial data. The present work investigated the practicality of a deep learning CNN-LSTM model within the Directional Change framework to predict significant DC events—a task crucial for timely decisionmaking in financial markets. Furthermore, the potential of autoencoders and variational autoencoders to enhance financial forecasting accuracy and remove noise from financial time series data was explored. Leveraging their capacity within financial time series, these models offered promising avenues for improved data representation and subsequent forecasting. To further contribute to financial prediction capabilities, a deep multi-model was developed that harnessed the power of pre-trained computer vision models. This innovative approach aimed to predict the VVIX, utilising the cross-disciplinary synergy between computer vision and financial forecasting. By integrating knowledge from these domains, novel insights into the prediction of market volatility were provided

Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies

Deep learning techniques have been widely applied in the field of stock market prediction particularly with respect to the implementation of active trading strategies. However, the area of portfolio management and passive portfolio management in particular has been much less well served by research to date. This research project conducts an investigation into the science underlying the implementation of portfolio management strategies in practice focusing on enhanced indexing strategies. Enhanced indexing is a passive management approach which introduces an element of active management with the aim of achieving a level of active return through small adjustments to the portfolio weights. It then proceeds to investigate current applications of deep learning techniques in the field of financial market predictions and also in the specific area of portfolio management. A series of successively deeper neural network models were then developed and assessed in terms of their ability to accurately predict whether a sample of stocks would either outperform or underperform the selected benchmark index. The predictions generated by these models were then used to guide the adjustment of portfolio weightings to implement and forward test an enhanced indexing strategy on a hypothetical stock portfolio

American Option Pricing using Self-Attention GRU and Shapley Value Interpretation

Options, serving as a crucial financial instrument, are used by investors to manage and mitigate their investment risks within the securities market. Precisely predicting the present price of an option enables investors to make informed and efficient decisions. In this paper, we propose a machine learning method for forecasting the prices of SPY (ETF) option based on gated recurrent unit (GRU) and self-attention mechanism. We first partitioned the raw dataset into 15 subsets according to moneyness and days to maturity criteria. For each subset, we matched the corresponding U.S. government bond rates and Implied Volatility Indices. This segmentation allows for a more insightful exploration of the impacts of risk-free rates and underlying volatility on option pricing. Next, we built four different machine learning models, including multilayer perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and self-attention GRU in comparison to the traditional binomial model. The empirical result shows that self-attention GRU with historical data outperforms other models due to its ability to capture complex temporal dependencies and leverage the contextual information embedded in the historical data. Finally, in order to unveil the "black box" of artificial intelligence, we employed the SHapley Additive exPlanations (SHAP) method to interpret and analyze the prediction results of the self-attention GRU model with historical data. This provides insights into the significance and contributions of different input features on the pricing of American-style options.Comment: Working pape