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On the Need for Speculative Philosophy Today  [PDF]
Andrew James Taggart
Cosmos and History : the Journal of Natural and Social Philosophy , 2012,
Abstract: ‘On the Need for Speculative Philosophy Today’ takes seriously Hegel’s claims that speculative philosophy begins in diremption and ends in higher-order conceptualization. To make Hegel’s theses more perspicuous, I examine the set of modern life needs—historical, metaphysical, phenomenological, and political—that give rise to speculative philosophy. I then attempt to show that speculative philosophy’s ultimate aim is to provide us with higher-order consolation. In the final section, I mean to draw on the second sense of speculation, conjecturing that rational form of inquiry I have undertaken is a propaedeutic to ‘philosophies of action’: philosophy of life and public philosophy.
The Speculative Nature of Stock Market  [PDF]
Bogdan-Gabriel FILIPESCU
Economia : Seria Management , 2011,
Abstract: This paper focuses on the speculative nature of the stock market in Romania, emphasizing the basic rules and risks associated with stock transactions. On the one hand, the speculative nature may be considered as a mandatory feature of the stock market, for the purposes of supporting a fair and efficient functioning stock system. On the other hand, the term "speculative" can be also interpreted in a negative direction, i.e. in combination with market manipulation or market abuse. Related to this latter interpretation, the study refers to European legislation on market abuse, accepted market practices and those that constitute market manipulation.
Chaotic behaviour in speculative markets
Fernández Rodríguez, Fernando,García Artiles, María Dolores
Rect@ , 2001,
Abstract: An asset price model of speculative financial market with fundamentalists and chartists is analyzed. Our model explains bursts of volatility in financial markets, which are not well explained by the traditional finance paradigms, as we will show. Depending on the time lag in the formation of chartists' expectations, the system evolves through several dynamic regimes finishing in a strange attractor. Chaos provides a self-sustained motion around the rationally expected equilibrium that corresponds to a speculative bubble. In order to explain the role of Chartism, chaotic motion is a very interesting theoretical feature for a speculative financial market model. It provides a complex non-linear dynamic behaviour around the Walrasian equilibrium price produced by deterministic interactions between fundamentalists and chartists
Artificial Agents and Speculative Bubbles  [PDF]
Yann Semet,Sylvain Gelly,Marc Schoenauer,Michèle Sebag
Computer Science , 2005,
Abstract: Pertaining to Agent-based Computational Economics (ACE), this work presents two models for the rise and downfall of speculative bubbles through an exchange price fixing based on double auction mechanisms. The first model is based on a finite time horizon context, where the expected dividends decrease along time. The second model follows the {\em greater fool} hypothesis; the agent behaviour depends on the comparison of the estimated risk with the greater fool's. Simulations shed some light on the influent parameters and the necessary conditions for the apparition of speculative bubbles in an asset market within the considered framework.
Speculative Symbolic Execution  [PDF]
Yufeng Zhang,Zhenbang Chen,Ji Wang
Computer Science , 2012,
Abstract: Symbolic execution is an effective path oriented and constraint based program analysis technique. Recently, there is a significant development in the research and application of symbolic execution. However, symbolic execution still suffers from the scalability problem in practice, especially when applied to large-scale or very complex programs. In this paper, we propose a new fashion of symbolic execution, named Speculative Symbolic Execution (SSE), to speed up symbolic execution by reducing the invocation times of constraint solver. In SSE, when encountering a branch statement, the search procedure may speculatively explore the branch without regard to the feasibility. Constraint solver is invoked only when the speculated branches are accumulated to a specified number. In addition, we present a key optimization technique that enhances SSE greatly. We have implemented SSE and the optimization technique on Symbolic Pathfinder (SPF). Experimental results on six programs show that, our method can reduce the invocation times of constraint solver by 21% to 49% (with an average of 30%), and save the search time from 23.6% to 43.6% (with an average of 30%).
Speculative Staging for Interpreter Optimization  [PDF]
Stefan Brunthaler
Computer Science , 2013,
Abstract: Interpreters have a bad reputation for having lower performance than just-in-time compilers. We present a new way of building high performance interpreters that is particularly effective for executing dynamically typed programming languages. The key idea is to combine speculative staging of optimized interpreter instructions with a novel technique of incrementally and iteratively concerting them at run-time. This paper introduces the concepts behind deriving optimized instructions from existing interpreter instructions---incrementally peeling off layers of complexity. When compiling the interpreter, these optimized derivatives will be compiled along with the original interpreter instructions. Therefore, our technique is portable by construction since it leverages the existing compiler's backend. At run-time we use instruction substitution from the interpreter's original and expensive instructions to optimized instruction derivatives to speed up execution. Our technique unites high performance with the simplicity and portability of interpreters---we report that our optimization makes the CPython interpreter up to more than four times faster, where our interpreter closes the gap between and sometimes even outperforms PyPy's just-in-time compiler.
Speculative Approximations for Terascale Analytics  [PDF]
Chengjie Qin,Florin Rusu
Computer Science , 2015,
Abstract: Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch for every dataset/model combination even by experienced data scientists. We argue that the incapacity to evaluate multiple parameter configurations simultaneously and the lack of support to quickly identify sub-optimal configurations are the principal causes. In this paper, we develop two database-inspired techniques for efficient model calibration. Speculative parameter testing applies advanced parallel multi-query processing methods to evaluate several configurations concurrently. The number of configurations is determined adaptively at runtime, while the configurations themselves are extracted from a distribution that is continuously learned following a Bayesian process. Online aggregation is applied to identify sub-optimal configurations early in the processing by incrementally sampling the training dataset and estimating the objective function corresponding to each configuration. We design concurrent online aggregation estimators and define halting conditions to accurately and timely stop the execution. We apply the proposed techniques to distributed gradient descent optimization -- batch and incremental -- for support vector machines and logistic regression models. We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big Data analytics system -- and evaluate their performance over terascale-size synthetic and real datasets. The results confirm that as many as 32 configurations can be evaluated concurrently almost as fast as one, while sub-optimal configurations are detected accurately in as little as a $1/20^{\text{th}}$ fraction of the time.
"Thermometers" of Speculative Frenzy  [PDF]
B. M. Roehner,D. Sornette
Quantitative Finance , 2000, DOI: 10.1007/s100510070190
Abstract: Establishing unambiguously the existence of speculative bubbles is an on-going controversy complicated by the need of defining a model of fundamental prices. Here, we present a novel empirical method which bypasses all the difficulties of the previous approaches by monitoring external indicators of an anomalously growing interest in the public at times of bubbles. From the definition of a bubble as a self-fulfilling reinforcing price change, we identify indicators of a possible self-reinforcing imitation between agents in the market. We show that during the build-up phase of a bubble, there is a growing interest in the public for the commodity in question, whether it consists in stocks, diamonds or coins. That interest can be estimated through different indicators: increase in the number of books published on the topic, increase in the subscriptions to specialized journals. Moreover, the well-known empirical rule according to which the volume of sales is growing during a bull market finds a natural interpretation in this framework: sales increases in fact reveal and pinpoint the progress of the bubble's diffusion throughout society. We also present a simple model of rational expectation which maps exactly onto the Ising model on a random graph. The indicators are then interpreted as ``thermometers'', measuring the balance between idiosyncratic information (noise temperature) and imitation (coupling) strength. In this context, bubbles are interpreted as low or critical temperature phases, where the imitation strength carries market prices up essentially independently of fundamentals. Contrary to the naive conception of a bubble and a crash as times of disorder, on the contrary, we show that bubbles and crashes are times where the concensus is too strong.
Speculative trading: the price multiplier effect  [PDF]
B. M. Roehner
Quantitative Finance , 1999, DOI: 10.1007/s100510050144
Abstract: During a speculative episode the price of an item jumps from an initial level p_1 to a peak level p_2 before more or less returning to level p_1. The ratio p_2/p_1 is referred to as the amplitude A of the peak. This paper shows that for a given market the peak amplitude is a linear function of the logarithm of the price at the beginning of the speculative episode; with p_1 expressed in 1999 euros the relationship takes the form: $ A=a\ln p_1 +b $; the values of the parameter a turn out to be relatively independent of the market considered: $ a \simeq 0.5 $, the values of the parameter b are more market-dependent, but are stable in the course of time for a given market. This relationship suggests that the higher the stakes the more "bullish" the market becomes. Possible mechanisms of this "risk affinity" effect are discussed.
A stochastic model for speculative bubbles  [PDF]
Sébastien Gadat,Laurent Miclo,Fabien Panloup
Quantitative Finance , 2013,
Abstract: This paper aims to provide a simple modelling of speculative bubbles and derive some quantitative properties of its dynamical evolution. Starting from a description of individual speculative behaviours, we build and study a second order Markov process, which after simple transformations can be viewed as a turning two-dimensional Gaussian process. Then, our main problem is to ob- tain some bounds for the persistence rate relative to the return time to a given price. In our main results, we prove with both spectral and probabilistic methods that this rate is almost proportional to the turning frequency {\omega} of the model and provide some explicit bounds. In the continuity of this result, we build some estimators of {\omega} and of the pseudo-period of the prices. At last, we end the paper by a proof of the quasi-stationary distribution of the process, as well as the existence of its persistence rate.
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