scholarly journals The Interaction between the Aggregate Behaviour of Technical Trading Systems and Stock Price Dynamics

2007 ◽  
Author(s):  
Stephan Schulmeister
Keyword(s):  
Stock Price ◽  
Price Dynamics ◽  
Technical Trading ◽  
Trading Systems

scholarly journals Memory effects in stock price dynamics: evidences of technical trading

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Federico Garzarelli ◽  
Matthieu Cristelli ◽  
Gabriele Pompa ◽  
Andrea Zaccaria ◽  
Luciano Pietronero
Keyword(s):  
Stock Price ◽  
Memory Effects ◽  
Price Dynamics ◽  
Technical Trading

scholarly journals Technical trading strategies, pattern recognition and weak-form market efficiency tests

2012 ◽  
Author(s):  
Πρόδρομος Τσινασλανίδης
Keyword(s):  
Cognitive Biases ◽  
Weak Form ◽  
Visual Assessment ◽  
Trading Strategies ◽  
Technical Trading ◽  
Trading Rules ◽  
Trading Systems ◽  
Finance Industry ◽  
Price Series ◽  
Real Price

Technical analysis (TA) is considered as an “economic” test for the random walk 2 hypothesis and thus for the weak form Efficiency Market Hypothesis (EMH). Advocates of TA assert that it is plausible to forecast future evolutions of financial assets‟ price paths with a bundle of technical tools conditioned on historical prices. Among these tools, we can identify technical patterns, which are specific forms of price paths‟ evolutions which are mainly identified visually. When such pattern is confirmed, a technician expects prices to evolve with a specific way. Although, bibliography on testing the efficacy of TA is massive, only a minor fraction of it deals with technical patterns. Various cognitive biases affecting practitioners‟ trading and investment activities and subjectivity embedded in the pattern‟s recognition process via visual assessment, set significant barriers in any attempt to evaluate the performance of trading strategies including such patterns. In this thesis we propose novel, rule-based, identification mechanisms for a set of well known technical patterns classified in the following three general categories: horizontal, zig-zag and circular patterns. The novelty of the proposed methodologies resides in the manner the identification mechanisms are designed. Core principles of TA, regarding the pattern identification via visual assessment are being quantified and the proposed recognizers outperform already existed ones to the fact that they identify all variations of the examined patterns regardless of their size, in a more objective manner. Thus, we believe that the proposed methodologies can set another basis for the development of more sophisticated automatic trading systems and more comprehensive and robust evaluations of TA in general. Implications for the industry and the finance community are also plausible. Software programs (or packages) of TA can include these recognizers in the bundle of all other technical indicators they provide within their services. Finally, practitioners may include these trading rules within their investment and trading activities, after assessing their performance individually, enhancing them (if necessary), or modifying them according to their idiosyncratic investment profile. We subsequently proceed to the individual and joint evaluation of the examined patterns‟ performance. For this purpose we use a variety of datasets (artificially created, US stocks and worldwide market indices) and assess generated returns with ordinary statistical tests, bootstrapped techniques and artificial neural networks. Our empirical findings are either new or comparable with already existed ones. To our point of view, some of the most significant and interesting are the followings: 1) Technical patterns were successfully identified in stochastically generated price paths. Thus, it is reasonable to expect their appearance in real price series too. 2) For specific patterns, when applied on stochastic price series, frequencies of observations, and returns‟ characteristics were similar with those observed in real price series. 3) Generally, our results are in favour of EMH. 4) Indications of market inefficiencies (if any) were more profound in the earlier sub-periods of examination, but not in recent ones. 5) Indications in favour of TA (if any) were observed when shorter holding periods were used. 6) Technical trading rules may successfully predict trend reversals, trend continuations or the sign of future returns, but they fail to generate systematically, statistically significant excess returns. The latter finding, if combined with a variety of cognitive biases included in investors‟ decision making processes, may reason for the apparent wide-spread implementation of TA within the everyday trading and investment activities of practitioners. This thesis is not the first published attempt to quantify such technical patterns and assess the generalised efficacy of TA. However, to our knowledge, the manner we approached the aforementioned issues is new. We believe that the proposed methodologies outperform already existed ones and implications of this thesis to academia and finance industry are significant.

scholarly journals An Interval of No-Arbitrage Prices in Financial Markets with Volatility Uncertainty

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Hanlei Hu ◽  
Zheng Yin ◽  
Weipeng Yuan
Keyword(s):  
Brownian Motion ◽  
Financial Markets ◽  
Stock Price ◽  
Contingent Claims ◽  
Price Dynamics ◽  
Complex Situation ◽  
No Arbitrage ◽  
Price Process ◽  
Arbitrage Opportunities

In financial markets with volatility uncertainty, we assume that their risks are caused by uncertain volatilities and their assets are effectively allocated in the risk-free asset and a risky stock, whose price process is supposed to follow a geometric G-Brownian motion rather than a classical Brownian motion. The concept of arbitrage is used to deal with this complex situation and we consider stock price dynamics with no-arbitrage opportunities. For general European contingent claims, we deduce the interval of no-arbitrage price and the clear results are derived in the Markovian case.

scholarly journals Scaling and Volatility of Breakouts and Breakdowns in Stock Price Dynamics

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82771 ◽  
Author(s):  
Lu Liu ◽  
Jianrong Wei ◽  
Jiping Huang
Keyword(s):  
Stock Price ◽  
Price Dynamics

The Procter and Gamble Stock Price Dynamics Analysis and Forecasting

2021 ◽  
pp. 128-136
Author(s):  
K. V. Gusev ◽  
Roman I. Dzerjinsky ◽  
P. A. Palamarchuk
Keyword(s):  
Stock Price ◽  
Price Dynamics ◽  
Dynamics Analysis

scholarly journals Alternative models for stock price dynamics

2003 ◽  
Vol 116 (1-2) ◽  
pp. 225-257 ◽  
Author(s):  
Mikhail Chernov ◽  
A. Ronald Gallant ◽  
Eric Ghysels ◽  
George Tauchen
Keyword(s):  
Stock Price ◽  
Price Dynamics ◽  
Alternative Models

Cash Flow News and Stock Price Dynamics

2020 ◽  
Vol 75 (4) ◽  
pp. 2221-2270 ◽  
Author(s):  
DAVIDE PETTENUZZO ◽  
RICCARDO SABBATUCCI ◽  
ALLAN TIMMERMANN
Keyword(s):  
Cash Flow ◽  
Stock Price ◽  
Price Dynamics ◽  
Cash Flow News

Monetary policy announcements and stock price dynamics in a small open economy

2011 ◽  
Vol 20 (4) ◽  
pp. 520-531 ◽  
Author(s):  
Chi-Chur Chao ◽  
Shih-Wen Hu ◽  
Meng-Yi Tai ◽  
Vey Wang
Keyword(s):  
Monetary Policy ◽  
Open Economy ◽  
Stock Price ◽  
Small Open Economy ◽  
Price Dynamics
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