Does Internet access to official data display any regularity: case of the Electronic Data Delivery System of the Central Bank of Turkey

1990s were the years of enormous growth of information exchange. Rapid development, augmented coverage and wide accessibility of Internet have been the key factors of that amazing growth. People’s access to economic and financial data was one of the major areas in which new trends and patterns of usage were observed. Owing to the elevated importance of financial information in today’s sophisticated markets, it is hypothesized that the linkage between data access patterns and economic events should display some regularity. In addition, one should be able to explain part of the irregularities. This study examines the access statistics of the Central Bank of Turkey’s Electronic Data Delivery System on these grounds. Using OLS and EGARCH models, significant evidence was obtained for the existence of regularities (i.e. calendar effects) in the data.Data access; Macroeconomic data; Return to information; Economics of information

Stock market efficiency in South Eastern Europe: testing return predictability and presence of calendar effects

This paper examines the calendar effects in ten South Eastern European (SEE) stock markets daily returns during the period 2007 - 2014. We focus on three calendar effects: the day of the week effect, the half month effect and the turn of the month effect. Specifically, we analyze existence of each calendar effect separately in the mean and in the volatility of the index returns. We apply standard regression models with dummy variables for the effects in the mean returns, while we apply GARCH(1,1) models with dummy variables for the effects in the volatility of returns. The results present evidence that the day of the week effects in both mean and volatility are present in nine out of ten SEE stock markets. Contrary, the half month effect in mean returns is present only in one SEE stock market, while half month effect in volatility is present in five out of ten SEE stock markets. The turn of the month effect in mean returns is present in six out of ten SEE stock markets. The turn of the month effect in volatility is present in all SEE stock markets

When the blockchain does not block : on hackings and uncertainty in the cryptocurrency market

A total of 1.1 million bitcoins were stolen in the 2013–2017 period. Noting that the average price for a Bitcoin in 2018 was $7572 the corresponding monetary equivalent of losses is$8.9 billion highlighting the societal impact of this criminal activity. Investigating the response of the uncertainty of Bitcoin returns when hacking incidents occur, the results of this study point toward two different responses. After experiencing a contemporaneous effect at day t=0, the volatility increases significantly again at day t+5. Hacking incidents that occur in the Bitcoin market also affect the uncertainty in the Ethereum market with a time delay of five days. Notably, neither Bitcoin nor Ethereum appear to exhibit asymmetric responses to negative innovations.© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.fi=vertaisarvioitu|en=peerReviewed

Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis

Abstract There is a growing trend towards data mining applications in medicine. Different algorithms have been explored by medical practitioners in an attempt to assist their work; the diagnosis of breast cancer is one of those applications. Machine learning algorithms are of vital importance to many medical problems, they can help to diagnose a disease, to detect its causes, to predict the outcome of a treatment, etc. K-Nearest Neighbors algorithm (KNN) is one of the simplest algorithms; it is widely used in predictive analysis. To optimize its performance and to accelerate its process, this paper proposes a new solution to speed up KNN algorithm based on clustering and attributes filtering. It also includes another improvement based on reliability coefficients which insures a more accurate classification. Thus, the contributions of this paper are three-fold: (i) the clustering of class instances, (ii) the selection of most significant attributes, and (iii) the ponderation of similarities by reliability coefficients. Results of the proposed approach exceeded most known classification techniques with an average f-measure exceeding 94% on the considered breast-cancer Dataset

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 Impact of the COVID-19 Pandemic and Russia's Invasion of Ukraine on Electricity Demand: A Case Study of Southern European Countries.

Recent global events, particularly the COVID-19 pandemic and Russia's invasion of Ukraine, have been found to dramatically influence electricity consumption patterns, especially within European nations. In this study, the impacts of these consecutive crises on the electricity demand of selected EU countries: Bulgaria, Greece, Romania, and different regions of Italy were examined. The Ordinary Least Squares regression model was utilized to analyze hourly load data and air temperatures. The findings indicate that the 2020 COVID-19 lockdown reduced consumption uniformly across the studied regions, while the 2022 energy crisis led to varied impacts, with distinct patterns being exhibited in regions within Italy. Remarkably, resilience was shown by Bulgaria during both crises, whereas pronounced effects were experienced in Southern Italy in both periods. The importance of understanding these shifts for effective policymaking and future resilience planning is emphasized in this study. A limitation of the analysis is found in its sole use of aggregate power load data and its generalized modelling. It is suspected that clearer results could be obtained in each case if analyzed the electricity consumption data separated by sectors

Stock Market Prediction via Deep Learning Techniques: A Survey

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction

Using machine learning to forecast long-term equity price movement : an empirical study of the Finnish financial markets

Predicting equity price movement is one of the fundamental challenges in finance, and even small improvements in prediction performance can be highly profitable for investors. Long-term investment is one of the popular investment strategies that investors follow. However, evaluating which companies are going to perform well in the future is difficult. This research presents machine learning aided approach to forecast long-term price movement of the stocks listed on the Helsinki Stock Exchange. The purpose of the research is to find out which machine learning model performs the best in the Finnish financial markets and to understand what the key variables are, which have a major effect on the prediction accuracy of the models. The research is also testing whether the macroeconomic variables of Finland increase the accuracy of the machine learning models when forecasting long-term equity price movement. The following machine learning models are used in the research: logistic regression, support vector machine, decision tree, random forest, and k-nearest neighbors. This research produced a number of key findings: the results from the models indicated that the best performance was achieved by the random forest model, which obtained classification accuracy of 65.3% and F1 score of 60.8%; the random forest model is able to give over 60% chance for an investor to pick a stock, which will have a 10% or higher return over the period of one year; the macroeconomic variables increased the prediction performance of every machine learning model used in the research. The main conclusions drawn from this research are that the macroeconomic variables can provide new information, which is not explained by only using financial ratios in the models. Also, the equity prices in the Finnish financial markets are not equally random, meaning that they do not always follow a random walk process. Therefore, this research argues that the Finnish financial market is not highly efficient, thus stock prices are on some level predictable. These findings contribute to the financial theory of market efficiency

Multi-Asset Factor Investing Strategies and Controversy Screening using Natural Language Processing

Factor investing strategies have revolutionized the landscape of equity investing, and continues to be heavily researched by academics and practitioners, leading to the documentation of more than 450 factors. However, from a practical investment perspective, much of the factor evidence documented by academics may be more apparent than real. The performance of many factors has found to be dependent on the inclusion of small- and micro-cap stocks in academic studies, although such stocks would likely be excluded from the real investment universe due to illiquidity and transaction costs. We take the perspective of an institutional investor and navigate this zoo of factors by focusing on the evidence relevant to the practicalities of factor-based investment strategies. Establishing a sound theoretical rationale is key to identifying “true” factors, and we emphasize the need to recognize data-mining concerns that may cast doubt on the relevance of many factors. Nevertheless, a parsimonious set of factors emerges in equities and other asset classes, including currencies, fixed income and commodities. Since these factors can serve as meaningful ingredients to factor-based portfolio construction, we build currency factor strategies using the G10 currencies. We show that parametric portfolio policies can help guide an optimal currency strategy when tilting towards cross-sectional factor characteristics. While currency carry serves as the main return generator in this tilting strategy, momentum and value are implicit diversifiers to potentially balance the downside of carry investing in flight-to-quality shifts of foreign exchange investors. Drawing insights from a currency timing strategy, according to time series predictors, we further examine the parametric portfolio policy’s ability to mitigate the downside of the carry trade by incorporating an explicit currency factor timing element. This integrated approach to currency factor investing outperforms a naive equally weighted benchmark as well as univariate and multivariate parametric portfolio policies. Whilst factor investing continues to grow in popularity, investors have expressed interest in aligning their investments with social values in order to maximize positive social impact. Hence, for any company, involvement in socially unethical practices not only leads to reputational damage but also financial consequences, anecdotally. To quantify the consequence of such controversial behaviour, we investigate the price impact of involvement in social controversies and find that the returns drop, on an average, by over 200 basis in the days around the outbreak of news on social violations. We identify companies following socially unethical practices from news headlines with the help of state-of-the-art language modelling approaches. Using a large sample of 1 million news headlines, we further train and fine-tune a DistilRoBERTa model to identify reports of controversial incidents in daily news feed. We map the price reaction of such controversial events using an event study approach and document negative price impact for companies with poor social practices measured via increased controversial behaviour, largely driven by small to medium market capitalization companies. Amongst the eight different social dimensions we examine, controversies surrounding violations of product safety standards, online scams and data privacy breaches significantly impact firm returns. Dissecting this result by geographies, the U.S, Australia, Europe and Emerging Market react very negatively to social controversies