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38
Economic Forecasting
- in Agriculture.” International Journal of Forecasting
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Corn: Correlation-driven nonparametric learning approach for portfolio selection
- ACM Transactions on Intelligent Systems and Technology
, 2011
"... Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this paper, we propose a novel learning to trade algorithm termed the CORrelation-driven Nonparametric learning strategy (CORN) for actively trading stocks ..."
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Cited by 6 (5 self)
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Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this paper, we propose a novel learning to trade algorithm termed the CORrelation-driven Nonparametric learning strategy (CORN) for actively trading stocks, which effectively exploits statistical relations between stock market windows via a nonparametric learning approach. We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, in which the encouraging results show that the proposed new algorithm can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpasses a variety of state-of-the-art techniques significantly.
Measuring the efficiency of the intraday forex market with a universal data compression algorithm
- Computational Economics
, 2009
"... Universal compression algorithms can detect recurring patterns in any type of temporal data – including financial data – for the purpose of compression. The universal algorithms actually find a model of the data that can be used for either compression or prediction. We present a universal Variable O ..."
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Cited by 5 (1 self)
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Universal compression algorithms can detect recurring patterns in any type of temporal data – including financial data – for the purpose of compression. The universal algorithms actually find a model of the data that can be used for either compression or prediction. We present a universal Variable Order Markov (VOM) model and use it to test of the weak form of the Efficient Market Hypothesis (EMH). The EMH is tested for 12 pairs of international intra-day currency exchange rates for one year series of 1,5,10,15,20,25 and 30 minutes. Statistically significant compression is detected in all the time-series and the high frequency series are also predictable above random. However, the predictability of the model is not sufficient to generate a profitable trading strategy, thus, Forex market turns out to be efficient, at least most of the time.
History of the Efficient Market Hypothesis
, 2011
"... A market is said to be efficient with respect to an information set if the price ‘fully reflects ’ that information set, i.e. if the price would be unaffected by revealing the information set to all market participants. The efficient market hypothesis (EMH) asserts that financial markets are efficie ..."
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Cited by 3 (0 self)
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A market is said to be efficient with respect to an information set if the price ‘fully reflects ’ that information set, i.e. if the price would be unaffected by revealing the information set to all market participants. The efficient market hypothesis (EMH) asserts that financial markets are efficient. On the one hand, the definitional ‘fully ’ is an exacting requirement, suggesting that no real market could ever be efficient, implying that the EMH is almost certainly false. On the other hand, economics is a social science, and a hypothesis that is asymptotically true puts the EMH in contention for one of the strongest hypotheses in the whole of the social sciences. Strictly speaking the EMH is false, but in spirit is profoundly true. Besides, science concerns seeking the best hypothesis, and until a flawed hypothesis is replaced by a better hypothesis, criticism is of limited value. Starting in the 16th century, this note gives a chronological review of the notable literature relating to the EMH. History of the Efficient Market Hypothesis
Putting a Price on Carbon – Econometric Essays on the European Union Emissions Trading Scheme and its Impacts
, 2013
"... Government ..."
PREDICTABILITY OF STOCK PRICE BEHAVIOUR IN SOUTH AFRICA: A NON-PARAMETRIC APPROACH
"... The predictability of stock returns has been an important issue in the finance literature over the years. Investors who manage to predict stock returns successfully should realise excess returns above the normal profit. However, the efficient market hypothesis (EMH) supports the view that securities ..."
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Cited by 1 (0 self)
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The predictability of stock returns has been an important issue in the finance literature over the years. Investors who manage to predict stock returns successfully should realise excess returns above the normal profit. However, the efficient market hypothesis (EMH) supports the view that securities markets are extremely efficient in reflecting information about individual stocks
Forecasting Exchange Rates with Fuzzy Granular Evolving Modeling for Trading Strategies
"... This paper addresses a fuzzy set based evolving modeling (FBeM) approach and the task of fore-casting exchange rates in order to perform trading strategies. FBeM is a granular computing tech-nique that uses fuzzy information granules to model nonstationary functions providing functional and linguist ..."
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This paper addresses a fuzzy set based evolving modeling (FBeM) approach and the task of fore-casting exchange rates in order to perform trading strategies. FBeM is a granular computing tech-nique that uses fuzzy information granules to model nonstationary functions providing functional and linguistic approximations. As an application, this work considers the BRL/USD exchange rate mar-ket data for the period from January 2000 to Oc-tober 2012. Comparisons in terms of goodness of fit and based on trading performance indicators in-cludes the granular model against a Multi-Layer Perceptron (MLP), an autoregressive moving aver-age (ARMA), a naïve strategy and some state of the art evolving fuzzy systems. Computational results suggest that the FBeM model statistically outper-forms the alternative approaches.
AND
, 2010
"... One of the key drivers of seasonal behaviour in time series is weather, most notably temperature. Very often weather effects are inter-correlated with the impact of other one-off events like public holidays or promotional activity. Due to the uncertainty that comes with short-term weather forecasts, ..."
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One of the key drivers of seasonal behaviour in time series is weather, most notably temperature. Very often weather effects are inter-correlated with the impact of other one-off events like public holidays or promotional activity. Due to the uncertainty that comes with short-term weather forecasts, the exact ef-fect of weather on weekly or daily sales has been given very little attention in the forecasting literature. The present study evaluates the impact of weather-driven adjustments to forecasts in a Brewing company. The forecasting team applies a decomposition approach, where: (a) an exponentially smoothing model is used in order to produce weekly sales forecasts; (b) an econometric model is built once a year in order to estimate the impact of 10-day ahead temperature changes in sales (as an input to this model, weekly weather forecasts from the Met office are used); (c) the sales forecasts from the former are adjusted based on the impacts from the latter, as well as for promotions, one-off events and regular seasonal behaviour. Empirical findings suggest that the weather adjustment mechanism improves the forecasting function in the company.
