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on Forecasting |
| By: | Watts, Duncan J; Beck, Emorie D; Bienenstock, Elisa Jayne (Arizona State University); Bowers, Jake; Frank, Aaron; Grubesic, Anthony; Hofman, Jake; Rohrer, Julia Marie (University of Leipzig); Salganik, Matthew |
| Abstract: | In this essay we make four interrelated points. First, we reiterate previous arguments (Kleinberg et al 2015) that forecasting problems are more common in social science than is often appreciated. From this observation it follows that social scientists should care about predictive accuracy in addition to unbiased or consistent estimation of causal relationships. Second, we argue that social scientists should be interested in prediction even if they have no interest in forecasting per se. Whether they do so explicitly or not, that is, causal claims necessarily make predictions; thus it is both fair and arguably useful to hold them accountable for the accuracy of the predictions they make. Third, we argue that prediction, used in either of the above two senses, is a useful metric for quantifying progress. Important differences between social science explanations and machine learning algorithms notwithstanding, social scientists can still learn from approaches like the Common Task Framework (CTF) which have successfully driven progress in certain fields of AI over the past 30 years (Donoho, 2015). Finally, we anticipate that as the predictive performance of forecasting models and explanations alike receives more attention, it will become clear that it is subject to some upper limit which lies well below deterministic accuracy for many applications of interest (Martin et al 2016). Characterizing the properties of complex social systems that lead to higher or lower predictive limits therefore poses an interesting challenge for computational social science. |
| Date: | 2018–10–31 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:u6vz5&r=all |
| By: | Davide Ferrari; Francesco Ravazzolo (Universitätt Bozen; Erasmus Universiteit Rotterdam; Norges Bank; Federal Reserve Bank of San Francisco; University of California Santa Cruz; Tinbergen Instituut; Handelshøyskolen BI; Centre for Applied Macro- and Petroleum economics (CAMP)); Joaquin L. Vespignani |
| Abstract: | This paper focuses on forecasting quarterly energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the global GDP. To deal with the information in this large database, we apply a dynamic factor model based on a penalized maximum likelihood approach that allows us to shrink parameters to zero and to estimate sparse factor loadings. The estimated latent factors show considerable sparsity and heterogeneity in the selected loadings across variables. When the model is extended to predict energy commodity prices up to four periods ahead, results indicate larger predictability relative to the benchmark random walk model for 1-quarter ahead for all energy commodities. In our application, the largest improvement in terms of prediction accuracy is observed when predicting gas prices from 1 to 4 quarters ahead. |
| Keywords: | Energy Prices; Forecasting; Dynamic Factor Model; Sparse Estimation; Penalized Maximum Likelihood |
| JEL: | C1 C5 C8 E3 Q4 |
| Date: | 2019–12–20 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddgw:86692&r=all |
| By: | Carlos Cesar Trucios-Maza; João H. G Mazzeu; Luis K. Hotta; Pedro L. Valls Pereira; Marc Hallin |
| Abstract: | General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in time series and have been successfully applied in many economic and financial applications. However, their performance in the presence of outliers has not been analysed yet. In this paper, we study the impact of additive outliers on the identification, estimation and forecasting performance of general dynamic factor models. Based on our findings, we propose robust identification, estimation and forecasting procedures. Our proposal is evaluated via Monte Carlo experiments and in empirical data. |
| Keywords: | Dimension reduction; Forecast; Jumps; Large panels |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:eca:wpaper:2013/298201&r=all |
| By: | Firuz Kamalov |
| Abstract: | Stock price prediction is a rich research topic that has attracted interest from various areas of science. The recent success of machine learning in speech and image recognition has prompted researchers to apply these methods to asset price prediction. The majority of literature has been devoted to predicting either the actual asset price or the direction of price movement. In this paper, we study a hitherto little explored question of predicting significant changes in stock price based on previous changes using machine learning algorithms. We are particularly interested in the performance of neural network classifiers in the given context. To this end, we construct and test three neural network models including multi-layer perceptron, convolutional net, and long short term memory net. As benchmark models we use random forest and relative strength index methods. The models are tested using 10-year daily stock price data of four major US public companies. Test results show that predicting significant changes in stock price can be accomplished with a high degree of accuracy. In particular, we obtain substantially better results than similar studies that forecast the direction of price change. |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1912.08791&r=all |
| By: | Sidra Mehtab; Jaydip Sen |
| Abstract: | Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange of India, and collect its daily price movement over a period of three years (2015 to 2017). Based on the data of 2015 to 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory - based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks and found extremely interesting results. |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1912.07700&r=all |
| By: | KONDO Satoshi; MIYAKAWA Daisuke; SHIRAKI Kengo; SUGA Miki; USUKI Teppei |
| Abstract: | This study investigates the usefulness of machine learning methods for detecting and forecasting accounting fraud. First, we aim to "detect" accounting fraud and confirm an improvement in detection performance. We achieve this by using machine learning, which allows high-dimensional feature space, compared with a classical parametric model, which is based on limited explanatory variables. Second, we aim to "forecast" accounting fraud, by using the same approach. This area has not been studied significantly in the past, yet we confirm a solid forecast performance. Third, we interpret the model by examining how estimated score changes with respect to change in each predictor. The validation is done on public listed companies in Japan, and we confirm that the machine learning method increases the model performance, and that higher interaction of predictors, which machine learning made possible, contributes to large improvement in prediction. |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:eti:dpaper:19103&r=all |