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on Forecasting |
| By: | Fabio Calonaci; George Kapetanios; Simon Price |
| Abstract: | We re-examine predictability of US stock returns. Theoretically well-founded models predict that stationary combinations of I (1) variables such as the dividend or earnings to price ratios or the consumption/asset/income relationship often known as CAY may predict returns. However, there is evidence that these relationships are unstable, and that allowing for discrete shifts in the unconditional mean (location shifts) can lead to greater predictability. It is unclear why there should be a small number of discrete shifts and we allow for more general instability in the predictors, characterised by smooth variation, using a method introduced by Giraitis, Kapetanios and Yates. This can remove persistent components from observed time series, that may otherwise account for the presence of near unit root type behaviour. Our methodology may therefore be seen as an alternative to the widely used IVX methods where there is strong persistence in the predictor. We apply this to the three predictors mentioned above in a sample from 1952 to 2019 (including the financial crisis but excluding the Covid pandemic) and find that modelling smooth instability improves predictability and forecasting performance and tends to outperform discrete location shifts, whether identified by in-sample Bai-Perron tests or Markov-switching models. |
| Keywords: | returns predictability, long horizons, instability |
| JEL: | G17 C53 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2022-04&r= |
| By: | Ogulcan E. Orsel; Sasha S. Yamada |
| Abstract: | In this work, we apply machine learning techniques to historical stock prices to forecast future prices. To achieve this, we use recursive approaches that are appropriate for handling time series data. In particular, we apply a linear Kalman filter and different varieties of long short-term memory (LSTM) architectures to historical stock prices over a 10-year range (1/1/2011 - 1/1/2021). We quantify the results of these models by computing the error of the predicted values versus the historical values of each stock. We find that of the algorithms we investigated, a simple linear Kalman filter can predict the next-day value of stocks with low-volatility (e.g., Microsoft) surprisingly well. However, in the case of high-volatility stocks (e.g., Tesla) the more complex LSTM algorithms significantly outperform the Kalman filter. Our results show that we can classify different types of stocks and then train an LSTM for each stock type. This method could be used to automate portfolio generation for a target return rate. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.03156&r= |
| By: | Pincheira, Pablo; Hardy, Nicolas |
| Abstract: | In this paper, we propose a correlation-based test for the evaluation of two competing forecasts. Under the null hypothesis of equal correlations with the target variable, we derive the asymptotic distribution of our test using the Delta method. This null hypothesis is not necessarily equivalent to the null of equal Mean Squared Prediction Errors (MSPE). Specifically, it might be the case that the forecast displaying the lowest MSPE also exhibits the lowest correlation with the target variable: this is known as "The MSPE paradox" (Pincheira and Hardy; 2021). In this sense, our approach should be seen as complementary to traditional tests of equality in MSPE. Monte Carlo simulations indicate that our test has good size and power. Finally, we illustrate the use of our test in an empirical exercise in which we compare two different inflation forecasts for a sample of OECD economies. We find more rejections of the null of equal correlations than rejections of the null of equality in MSPE. |
| Keywords: | Forecasting, time-series, out-of-sample evaluation, mean squared prediction error, correlations. |
| JEL: | C52 C53 E31 E37 F37 G17 |
| Date: | 2022–02–16 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:112014&r= |
| By: | Udenio, Maximiliano; Vatamidou, Eleni; Fransoo, Jan C. (Tilburg University, School of Economics and Management) |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutis:8fca6329-83b9-4a49-a2aa-e4e959d4f761&r= |