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on Market Microstructure |
| By: | Nicholas Murphy; Tim Gebbie |
| Abstract: | We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an offline benchmark portfolio algorithm for performance comparison. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective. |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1903.02228&r=all |
| By: | Yoosoon Chang (Indiana University); Ye Lu (School of Economics University of Sydney); Joon Park (Department of Economics Indiana University and Sungkyunkwan University) |
| Abstract: | In this paper, we analyze regressions with observations collected at small time intervals over a long period of time. For the formal asymptotic analysis, we assume that samples are obtained from continuous time stochastic processes, and let the sampling interval δ shrink down to zero and the sample span T increase up to infinity. In this setup, we show that the standard Wald statistic diverges to infinity and the regression becomes spurious as long as δ → 0 sufficiently fast relative to T → ∞. Such a phenomenon is indeed what is frequently observed in practice for the type of regressions considered in the paper. In contrast, our asymptotic theory predicts that the spuriousness disappears if we use the robust version of the Wald test with an appropriate longrun variance estimate. This is supported, strongly and unambiguously, by our empirical illustration. |
| Keywords: | high frequency regression; spurious regression; continuous time model; asymptotics; longrun variance estimation |
| JEL: | C13 C22 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:inu:caeprp:2019001&r=all |
| By: | Jamshid Ardalankia; Mohammad Osoolian; Emmanuel Haven; G. Reza Jafari |
| Abstract: | Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there is mutual information and entanglement between price and volume. On the other hand, we are curious about scaling features of this entanglement and need to know how structures in different scales translate information. So, markets are faced with a variety of dimensions of price and trading volume. Investment size (volume), price-wise expectations (gain/loss), and time-wise expectations (time-scale) differ from one investor to another. This study, by applying the MF-DXA method, demonstrates that price and trading volume and their coupling contain power law information and are multifractal in the markets we investigated. Also, the resultant correlation coefficients present scaling behaviors which are totally significant in the investigated time-scales and they decrease with increasing time-scales. Meanwhile, considering developed markets, the price-volume coupling is more dominated by trading volume rather than price. This domination increases price validity. We can confirm that in a developed market, traders, with a certain price, are more rational and show their enthusiasm to price by applying trading volume. This approach for emerging markets is weak. As a whole, in emerging markets, market behavior is guided by a phenomenon other than volume. |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1903.01744&r=all |
| By: | Michael Greinecker (University of Graz, Austria); Christoph Kuzmics (University of Graz, Austria) |
| Abstract: | Investors who maximize subjective expected utility will generally trade in an asset unless the market price exactly equals the expected return, but few people participate in the stock market. [Dow and da Costa Werlang, Econometrica 1992] show that an ambiguity averse decision maker might abstain from trading in an asset for a wide interval of prices and use this fact to explain the lack of participation in the stock market. We show that when markets operate via limit orders, all investment behavior will be observationally equivalent to maximizing subjective expected utility; ambiguity aversion has no additional explanatory power. |
| Keywords: | Ambiguity; Knightian uncertainty; Dominance; Stock market participation; Limit orders |
| JEL: | D81 D83 G11 G12 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:grz:wpaper:2019-03&r=all |
| By: | L. Ingber |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:lei:ingber:19db&r=all |