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on Computational Economics |
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Issue of 2021‒04‒05
28 papers chosen by |
| By: | Ariel Neufeld; Julian Sester |
| Abstract: | We introduce a novel and highly tractable supervised learning approach based on neural networks that can be applied for the computation of model-free price bounds of, potentially high-dimensional, financial derivatives and for the determination of optimal hedging strategies attaining these bounds. In particular, our methodology allows to train a single neural network offline and then to use it online for the fast determination of model-free price bounds of a whole class of financial derivatives with current market data. We show the applicability of this approach and highlight its accuracy in several examples involving real market data. Further, we show how a neural network can be trained to solve martingale optimal transport problems involving fixed marginal distributions instead of financial market data. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.11435&r=all |
| By: | Huanming Zhang; Zhengyong Jiang; Jionglong Su |
| Abstract: | With the improvement of computer performance and the development of GPU-accelerated technology, trading with machine learning algorithms has attracted the attention of many researchers and practitioners. In this research, we propose a novel portfolio management strategy based on the framework of Deep Deterministic Policy Gradient, a policy-based reinforcement learning framework, and compare its performance to that of other trading strategies. In our framework, two Long Short-Term Memory neural networks and two fully connected neural networks are constructed. We also investigate the performance of our strategy with and without transaction costs. Experimentally, we choose eight US stocks consisting of four low-volatility stocks and four high-volatility stocks. We compare the compound annual return rate of our strategy against seven other strategies, e.g., Uniform Buy and Hold, Exponential Gradient and Universal Portfolios. In our case, the compound annual return rate is 14.12%, outperforming all other strategies. Furthermore, in terms of Sharpe Ratio (0.5988), our strategy is nearly 33% higher than that of the second-best performing strategy. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.11455&r=all |
| By: | Kollár, Aladár |
| Abstract: | In today's modern world, sports generate a great deal of data about each athlete, team, event, and season. Many people, from spectators to bettors, find it fascinating to predict the outcomes of sporting events. With the available data, the sports betting industry is turning to Artificial Intelligence. Working with a great deal of data and information is needed in sports betting all over the world. Artificial intelligence and machine learning are assisting in the prediction of sporting trends. The true influence of technology is felt as it offers these observations in real-time, which can have an impact on important factors in betting. An artificial neural network is made up of several small, interconnected processors called neurons, which are similar to the biological neurons in the brain. In ANN framework, MLP, the most applicable NN algorithm, are generally selected as the best model for predicting the outcomes of football matches. This review also discussed another common technique of modern intelligent technique, namely Support Vector Machines (SVM). Lastly, we also discussed the Markov chain to predict the result of a sport. Markov chain is the sequence or chain from which the next sample from this state space is sampled. |
| Keywords: | Artificial Intelligence; ANN; Betting; sports; SVM; Markov chain |
| JEL: | C5 C55 C6 |
| Date: | 2021–03–21 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:106821&r=all |
| By: | Mohammadreza Ghanbari; Mahdi Goldani |
| Abstract: | Support vector machine modeling is a new approach in machine learning for classification showing good performance on forecasting problems of small samples and high dimensions. Later, it promoted to Support Vector Regression (SVR) for regression problems. A big challenge for achieving reliable is the choice of appropriate parameters. Here, a novel Golden sine algorithm (GSA) based SVR is proposed for proper selection of the parameters. For comparison, the performance of the proposed algorithm is compared with eleven other meta-heuristic algorithms on some historical stock prices of technological companies from Yahoo Finance website based on Mean Squared Error and Mean Absolute Percent Error. The results demonstrate that the given algorithm is efficient for tuning the parameters and is indeed competitive in terms of accuracy and computing time. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.11459&r=all |
| By: | Yiyan Huang; Cheuk Hang Leung; Xing Yan; Qi Wu |
| Abstract: | Most existing studies on the double/debiased machine learning method concentrate on the causal parameter estimation recovering from the first-order orthogonal score function. In this paper, we will construct the $k^{\mathrm{th}}$-order orthogonal score function for estimating the average treatment effect (ATE) and present an algorithm that enables us to obtain the debiased estimator recovered from the score function. Such a higher-order orthogonal estimator is more robust to the misspecification of the propensity score than the first-order one does. Besides, it has the merit of being applicable with many machine learning methodologies such as Lasso, Random Forests, Neural Nets, etc. We also undergo comprehensive experiments to test the power of the estimator we construct from the score function using both the simulated datasets and the real datasets. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.11869&r=all |
| By: | Steve J. Bickley; Ho Fai Chan; Sascha L. Schmidt; Benno Torgler |
| Abstract: | Despite the great promises and potential of quantum computing, the full range of possibilities and practical applications is not yet clear. In this contribution, we highlight the potential for quantum theories and computation to reignite the art and science of expert systems and knowledge engineering. With their grounding in uncertainty and unpredictability, quantum concepts are able to expand theoretical and practical boundaries of research and exploration. We demonstrate the advantages of using quantum logic and computation when applying expert knowledge such as the ability to ask imprecise (inherently probabalistic) queries and do so e complicated computational spaces by leveraging quantum concepts such as superposition, entanglement, parallelism, and interference to sustain and manipulate networks of quantum qubits. This, we argue, can provide non-insignificant computational speedups and enable more complex data representations and analysis. We then explore three practical applications and provide evidence for their quantum advantages – Scientific Discovery, Creativity and Breakthrough Thinking, Complex Systems, and Future of Sports – that we regard as largely unexplored in popular accounts of quantum theory; as such, we suggest these are low hanging fruit for the more-immediately feasible applications of quantum technologies. |
| Keywords: | Quantum; Expert Systems; Knowledge; Problem-Solving |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:cra:wpaper:2021-14&r=all |
| By: | Tomasz Antczak; Bartosz Skorupa; Mikolaj Szurlej; Rafal Weron; Jacek Zabawa |
| Abstract: | We build an agent-based model for evaluating the spatial and functional design of supermarket checkout zones and the effectiveness of safety regulations related to distancing that have been introduced after the COVID-19 outbreak. The model is implemented in the NetLogo simulation platform and calibrated to actual point of sale data from one of major European retail chains. It enables realistic modeling of the checkout operations as well as of the airborne diffusion of SARS-CoV-2 particles. We find that opening checkouts in a specific order can reduce epidemic risk, but only under low and moderate traffic conditions. Hence, redesigning supermarket layouts to increase distances between the queues can reduce risk only if the number of open checkouts is sufficient to serve customers during peak hours. |
| Keywords: | Agent-based model; Indoor infection spreading; Checkout zone architecture; Decision support; COVID-19; NetLogo |
| JEL: | C15 C63 L81 |
| Date: | 2021–03–29 |
| URL: | http://d.repec.org/n?u=RePEc:ahh:wpaper:worms2105&r=all |
| By: | Aleksy Klimowicz (Faculty of Economic Sciences, University of Warsaw); Krzysztof Spirzewski (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | Numerous applications of AI are found in the banking sector. Starting from front-office, enhancing customer recognition and personalized services, continuing in middle-office with automated fraud-detection systems, ending with back-office and internal processes automatization. In this paper we provide comprehensive information on the phenomenon of peer-to-peer lending in the modern view of alternative finance and crowdfunding from several perspectives. The aim of this research is to explore the phenomenon of peer-to-peer lending market model. We apply and check the suitability and effectiveness of credit scorecards in the marketplace lending along with determining the appropriate cut-off point. We conducted this research by exploring recent studies and open-source data on marketplace lending. The scorecard development is based on the P2P loans open dataset that contains repayments record along with both hard and soft features of each loan. The quantitative part consists of applying a machine learning algorithm in building a credit scorecard, namely logistic regression. |
| Keywords: | artificial intelligence, peer-to-peer lending, credit risk assessment, credit scorecards, logistic regression, machine learning |
| JEL: | G21 C25 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2021-04&r=all |
| By: | Karush Suri; Xiao Qi Shi; Konstantinos Plataniotis; Yuri Lawryshyn |
| Abstract: | Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real world problems, RL is seldomly used in practical scenarios consisting of abrupt dynamics. This allows one to rethink the problem setup in light of practical challenges. We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem. Through this lens, TradeR makes use of hierarchical RL to execute trade bids on high frequency real market experiences comprising of abrupt price variations during the 2019 fiscal year COVID19 stock market crash. The framework utilizes an energy-based scheme in conjunction with surprise value function for estimating and minimizing surprise. In a large-scale study of 35 stock symbols from the S&P500 index, TradeR demonstrates robustness to abrupt price changes and catastrophic losses while maintaining profitable outcomes. We hope that our work serves as a motivating example for application of RL to practical problems. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.00620&r=all |
| By: | Mukul Jaggi; Priyanka Mandal; Shreya Narang; Usman Naseem; Matloob Khushi |
| Abstract: | Stock price prediction can be made more efficient by considering the price fluctuations and understanding the sentiments of people. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets were labelled with three labelling techniques based on stock price changes. Our proposed model FinALBERT is fine-tuned with these labels to achieve optimal results. We experimented with the labelled dataset by training it on traditional machine learning, BERT, and FinBERT models, which helped us understand how these labels behaved with different model architectures. Our labelling method competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.16388&r=all |
| By: | Hamid Bekamiri; Daniel S. Hain; Roman Jurowetzki |
| Abstract: | Purpose: This study aims to provide a hybrid approach for patent claim classification with Sentence-BERT (SBERT) and K Nearest Neighbours (KNN) and explicitly focuses on the patent claims. Patent classification is a multi-label classification task in which the number of labels can be greater than 640 at the subclass level. The proposed framework predicts individual input patent class and subclass based on finding top k semantic similarity patents. Design/Methodology/Approach: The study uses transformer models based on Augmented SBERT and RoBERTa. We use a different approach to predict patent classification by finding top k similar patent claims and using the KNN algorithm to predict patent class or subclass. Besides, in this study, we just focus on patent claims, and in the future study, we add other appropriate parts of patent documents. Findings: The findings suggest the relevance of hybrid models to predict multi-label classification based on text data. In this approach, we used the Transformer model as the distance function in KNN, and proposed a new version of KNN based on Augmented SBERT. Practical Implications: The presented framework provides a practical model for patent classification. In this study, we predict the class and subclass of the patent based on semantic claims similarity. The end-user interpretability of the results is one of the essential positive points of the model. Originality/Value: The main contribution of the study included: 1) Using the Augmented approach for fine-tuning SBERT by in-domain supervised patent claims data. 2) Improving results based on a hybrid model for patent classification. The best result of F1-score at the subclass level was > 69%) Proposing the practical model with high interpretability of results. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.11933&r=all |
| By: | Hans Genberg (Asia School of Business); Özer Karagedikli (South East Asian Central Banks (SEACEN) Research and Training Centre and Centre for Applied Macroeconomic Analysis (CAMA)) |
| Abstract: | In this article we review what machine learning might have to offer central banks as an analytical approach to support monetary policy decisions. After describing the central bank’s “problem†and providing a brief introduction to machine learning, we propose to use the gradual adoption of Vector Auto Regression (VAR) methods in central banks to speculate how machine learning models must (will?) evolve to become influential analytical tools supporting central banks’ monetary policy decisions. We argue that VAR methods achieved that status only after they incorporated elements that allowed users to interpret them in terms of structural economic theories. We believe that the same has to be the case for machine learning model. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:sea:wpaper:wp43&r=all |
| By: | Jay Cao; Jacky Chen; John Hull; Zissis Poulos |
| Abstract: | A common approach to valuing exotic options involves choosing a model and then determining its parameters to fit the volatility surface as closely as possible. We refer to this as the model calibration approach (MCA). This paper considers an alternative approach where the points on the volatility surface are features input to a neural network. We refer to this as the volatility feature approach (VFA). We conduct experiments showing that VFA can be expected to outperform MCA for the volatility surfaces encountered in practice. Once the upfront computational time has been invested in developing the neural network, the valuation of exotic options using VFA is very fast. VFA is a useful tool for the estimation of model risk. We illustrate this using S&P 500 data for the 2001 to 2019 period. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.12551&r=all |
| By: | Olivier Durand-Lasserve (King Abdullah Petroleum Studies and Research Center) |
| Abstract: | Gulf Cooperation Council (GCC) countries aim to employ more of their nationals in the private sector to absorb the inflow of new entrants into the labor force. They have put in place workforce nationalization policies to revert two peculiar features of their labor markets: the preference of nationals for public sector careers, and the crowding out of nationals by expatriate workers in the private sector. |
| Keywords: | Agent based modeling, Bargaining modeling, Behavior analysis, Collective decision making processes |
| Date: | 2021–03–15 |
| URL: | http://d.repec.org/n?u=RePEc:prc:dpaper:ks--2021-dp05&r=all |
| By: | Walid Matar (King Abdullah Petroleum Studies and Research Center) |
| Abstract: | This study explores the potential effects of real-time electricity pricing on the operations of Saudi Arabia’s power generation sector. The Kingdom currently sets fuel prices for power utilities at levels that suppress the costs of power generation. However, this analysis provides insights into the effects of a real-time electricity pricing scheme in the context of liberalized fuel prices. |
| Keywords: | Agent based modeling, Bargaining modeling, Behavior analysis, Collective decision making processes |
| Date: | 2021–03–22 |
| URL: | http://d.repec.org/n?u=RePEc:prc:dpaper:ks--2021-dp03&r=all |
| By: | Q. Wang; Y. Zhou; J. Shen |
| Abstract: | This article comes up with an intraday trading strategy under T+1 using Markowitz optimization and Multilayer Perceptron (MLP) with published stock data obtained from the Shenzhen Stock Exchange and Shanghai Stock Exchange. The empirical results reveal the profitability of Markowitz portfolio optimization and validate the intraday stock price prediction using MLP. The findings further combine the Markowitz optimization, an MLP with the trading strategy, to clarify this strategy's feasibility. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.13507&r=all |
| By: | Hannes Mueller; Christopher Rauh |
| Abstract: | There is a growing interest in prevention in several policy areas and this provides a strong motivation for an improved integration of forecasting with machine learning into models of decision making. In this article we propose a framework to tackle conflict prevention. A key problem of conflict forecasting for prevention is that predicting the start of conflict in previously peaceful countries needs to overcome a low baseline risk. To make progress in this hard problem this project combines a newspaper-text corpus of more than 4 million articles with unsupervised and supervised machine learning. The output of the forecast model is then integrated into a simple static framework in which a decision maker decides on the optimal number of interventions to minimize the total cost of conflict and intervention. This exercise highlights the potential cost savings of prevention for which reliable forecasts are a prerequisite. |
| Keywords: | armed conflict, forecasting, machine learning, newspaper text, random forest, topic models |
| JEL: | O11 O43 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:1244&r=all |
| By: | Seema Jayachandran; Monica Biradavolu; Jan Cooper |
| Abstract: | We propose a new method to design a short survey measure of a complex concept such as women's agency. The approach combines mixed-methods data collection and machine learning. We select the best survey questions based on how strongly correlated they are with a "gold standard'' measure of the concept derived from qualitative interviews. In our application, we measure agency for 209 women in Haryana, India, first, through a semi-structured interview and, second, through a large set of close-ended questions. We use qualitative coding methods to score each woman's agency based on the interview, which we treat as her true agency. To identify the close-ended questions most predictive of the "truth," we apply statistical algorithms that build on LASSO and random forest but constrain how many variables are selected for the model (five in our case). The resulting five-question index is as strongly correlated with the coded qualitative interview as is an index that uses all of the candidate questions. This approach of selecting survey questions based on their statistical correspondence to coded qualitative interviews could be used to design short survey modules for many other latent constructs. |
| JEL: | C83 D13 J16 O12 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28626&r=all |
| By: | Dave Cliff |
| Abstract: | This brief technical note introduces PRZI (Parameterised-Response Zero Intelligence), a new form of zero-intelligence trader intended for use in simulation studies of auction markets. Like Gode & Sunder's classic Zero-Intelligence Constrained (ZIC) trader, PRZI generates quote-prices from a random distribution over some specified domain of discretely-valued allowable quote-prices. Unlike ZIC, which uses a uniform distribution to generate prices, the probability distribution in a PRZI trader is parameterised in such a way that its probability mass function (PMF) is determined by a real-valued control variable s in the range [-1.0, +1.0] that determines the strategy for that trader. When s is zero, a PRZI trader behaves identically to the ZIC strategy, with a flat/rectangular PMF; but when s is close to plus or minus one the PRZI trader's PMF becomes asymptotically maximally skewed to one extreme or the other of the price-range, thereby enabling the PRZI trader to act in the same way as the "Shaver" strategy (SHVR) or the "Giveaway" strategy (GVWY), both of which have recently been demonstrated to be surprisingly dominant over more sophisticated, and supposedly more profitable, trader-strategies that incorporate adaptive mechanisms and machine learning. Depending on the value of s, a PRZI trader will behave either as a ZIC, or as a SHVR, or as a GVWY, or as some hybrid strategy part-way between two of these three previously-reported strategies. The novel smoothly-varying strategy in PRZI has value in giving trader-agents plausibly useful "market impact" responses to imbalances in an auction-market's limit-order-book, and also allows for the study of co-adaptive dynamics in continuous strategy-spaces rather than the discrete spaces that have traditionally been studied in the literature. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.11341&r=all |
| By: | Artur Sokolovsky; Luca Arnaboldi; Jaume Bacardit; Thomas Gross |
| Abstract: | Financial markets are a source of non-stationary multidimensional time series which has been drawing attention for decades. Each financial instrument has its specific changing over time properties, making their analysis a complex task. Improvement of understanding and development of methods for financial time series analysis is essential for successful operation on financial markets. In this study we propose a volume-based data pre-processing method for making financial time series more suitable for machine learning pipelines. We use a statistical approach for assessing the performance of the method. Namely, we formally state the hypotheses, set up associated classification tasks, compute effect sizes with confidence intervals, and run statistical tests to validate the hypotheses. We additionally assess the trading performance of the proposed method on historical data and compare it to a previously published approach. Our analysis shows that the proposed volume-based method allows successful classification of the financial time series patterns, and also leads to better classification performance than a price action-based method, excelling specifically on more liquid financial instruments. Finally, we propose an approach for obtaining feature interactions directly from tree-based models on example of CatBoost estimator, as well as formally assess the relatedness of the proposed approach and SHAP feature interactions with a positive outcome. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.12419&r=all |
| By: | Jay Cao; Jacky Chen; John Hull; Zissis Poulos |
| Abstract: | This paper shows how reinforcement learning can be used to derive optimal hedging strategies for derivatives when there are transaction costs. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedging for a short position in a call option when the objective is to minimize a function equal to the mean hedging cost plus a constant times the standard deviation of the hedging cost. Two situations are considered. In the first, the asset price follows a geometric Brownian motion. In the second, the asset price follows a stochastic volatility process. The paper extends the basic reinforcement learning approach in a number of ways. First, it uses two different Q-functions so that both the expected value of the cost and the expected value of the square of the cost are tracked for different state/action combinations. This approach increases the range of objective functions that can be used. Second, it uses a learning algorithm that allows for continuous state and action space. Third, it compares the accounting P&L approach (where the hedged position is valued at each step) and the cash flow approach (where cash inflows and outflows are used). We find that a hybrid approach involving the use of an accounting P&L approach that incorporates a relatively simple valuation model works well. The valuation model does not have to correspond to the process assumed for the underlying asset price. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.16409&r=all |
| By: | Jon Ellingsen; Vegard H. Larsen; Leif Anders Thorsrud |
| Abstract: | Using a unique dataset of 22.5 million news articles from the Dow Jones Newswires Archive, we perform an in depth real-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature, namely the FRED-MD dataset. Focusing on U.S. GDP, consumption and investment growth, our results suggest that the news data contains information not captured by the hard economic indicators, and that the news-based data are particularly informative for forecasting consumption developments. |
| Keywords: | forecasting, real-time, machine learning, news, text data |
| JEL: | C53 C55 E27 E37 |
| Date: | 2020–10–08 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2020_14&r=all |
| By: | Szekeres, Szabolcs |
| Abstract: | Discounting project net flows that exclude financing costs with prescriptive rates fails to reflect costs of capital; discounting them with descriptive rates fails to reflect intertemporal preferences. A hybrid discounting method is proposed whereby descriptive rates are used to forecast costs of capital and prescriptive rates are used to discount all-inclusive net welfare flows. An agent-based capital market model audits the performance of alternative discounting approaches. There is no need to reconcile the discounting approaches. They should be viewed as complementary, not as competing. They are both necessary, and only jointly sufficient to achieve optimality in intertemporal resource allocation. |
| Keywords: | Social discount rate; Prescriptive discounting; Descriptive discounting; Hybrid discounting; Declining discount rates. |
| JEL: | D61 H43 |
| Date: | 2021–02–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:106029&r=all |
| By: | Victor DeMiguel; Javier Gil-Bazo; Francisco J. Nogales; André A. P. Santos |
| Abstract: | Identifying outperforming mutual funds ex-ante is a notoriously difficult task. We use machine learning methods to exploit the predictive ability of a large set of mutual fund characteristics that are readily available to investors. Using data on US equity funds in the 1980-2018 period, the methods allow us to construct portfolios of funds that earn positive and significant out-of-sample risk-adjusted after-fee returns as high as 4.2% per year. We further show that such outstanding performance is the joint outcome of both exploiting the information contained in multiple fund characteristics and allowing for flexibility in the relationship between predictors and fund performance. Our results confirm that even retail investors can benefit from investing in actively managed funds. However, we also find that the performance of all our portfolios has declined over time, consistent with increased competition in the asset market and diseconomies of scale at the industry level. |
| Keywords: | mutual fund performance, performance predictability, active management, machine learning, elastic net, random forests, gradient boosting |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:1245&r=all |
| By: | Victor DeMiguel; Javier Gil-Bazo; Francisco J. Nogales; André A. P. Santos |
| Abstract: | Identifying outperforming mutual funds ex-ante is a notoriously difficult task. We use machine learning methods to exploit the predictive ability of a large set of mutual fund characteristics that are readily available to investors. Using data on US equity funds in the 1980-2018 period, the methods allow us to construct portfolios of funds that earn positive and significant out-of-sample risk-adjusted after-fee returns as high as 4.2% per year. We further show that such outstanding performance is the joint outcome of both exploiting the information contained in multiple fund characteristics and allowing for flexibility in the relationship between predictors and fund performance. Our results confirm that even retail investors can benefit from investing in actively managed funds. However, we also find that the performance of all our portfolios has declined over time, consistent with increased competition in the asset market and diseconomies of scale at the industry level. |
| Keywords: | Mutual fund performance, performance predictability, active management, machine learning, elastic net, random forests, gradient boosting |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1772&r=all |
| By: | Hanjo Odendaal (Department of Economics, Stellenbosch University) |
| Abstract: | This paper aims to offer an alternative to the manually labour intensive process of constructing a domain specific lexicon or dictionary through the operationalization of subjective information processing. This paper builds on current empirical literature by (a) constructing a domain specific dictionary for various economic confidence indices, (b) introducing a novel weighting schema of text tokens that account for time dependence; and (c) operationalising subjective information processing of text data using machine learning. The results show that sentiment indices constructed from machine generated dictionaries have a better fit with multiple indicators of economic activity than @loughran2011liability's manually constructed dictionary. Analysis shows a lower RMSE for the domain specific dictionaries in a five year holdout sample period from 2012 to 2017. The results also justify the time series weighting design used to overcome the p>>n problem, commonly found when working with economic time series and text data. |
| Keywords: | Sentometrics, Machine learning, Domain-specific dictionaries |
| JEL: | C32 C45 C53 C55 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:sza:wpaper:wpapers366&r=all |
| By: | Shan Huang; Michael Allan Ribers; Hannes Ullrich |
| Abstract: | Large-scale data show promise to provide efficiency gains through individualized risk predictions in many business and policy settings. Yet, assessments of the degree of data-enabled efficiency improvements remain scarce. We quantify the value of the availability of a variety of data combinations for tackling the policy problem of curbing antibiotic resistance, where the reduction of inefficient antibiotic use requires improved diagnostic prediction. Fousing on antibiotic prescribing for suspected urinary tract infections in primary care in Denmark, we link individual-level administrative data with microbiological laboratory test outcomes to train a machine learning algorithm predicting bacterial test results. For various data combinations, we assess out of sample prediction quality and efficiency improvements due to prediction-based prescription policies. The largest gains in prediction quality can be achieved using simple characteristics such as patient age and gender or patients’ health care data. However, additional patient background data lead to further incremental policy improvements even though gains in prediction quality are small. Our findings suggest that evaluating prediction quality against the ground truth only may not be sufficient to quantify the potential for policy improvements. |
| Keywords: | Prediction policy; data combination; machine learning; antibiotic prescribing |
| JEL: | C10 C55 I11 I18 Q28 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1939&r=all |
| By: | Jørgen Vitting Andersen (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, CNRS - Centre National de la Recherche Scientifique); Andrzej Nowak (Department of Psychology, Warsaw university - UW - University of Warsaw) |
| Abstract: | It is hard to overstate the importance that the concept of symmetry has had in every field of physics, a fact alluded to by the Nobel Prize winner P.W. Anderson, who once wrote that "physics is the study of symmetry". Whereas the idea of symmetry is widely used in science in general, very few (if not almost no) applications has found its way into the field of finance. Still, the phenomenon appears relevant in terms of for example the symmetry of strategies that can happen in the decision making to buy or sell financial shares. Game theory is therefore one obvious avenue where to look for symmetry, but as will be shown, also technical analysis and long term economic growth could be phenomena which show the hallmark of a symmetry. |
| Keywords: | Agent-based modelling,Game theory,Ginzburg-Landau theory,financial symmetry |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-03048686&r=all |