Two centuries of trend following

We establish the existence of anomalous excess returns based on trend following strategies across four asset classes (commodities, currencies, stock indices, bonds) and over very long time scales. We use for our studies both futures time series, that exist since 1960, and spot time series that allow us to go back to 1800 on commodities and indices. The overall t-stat of the excess returns is $\approx 5$ since 1960 and $\approx 10$ since 1800, after accounting for the overall upward drift of these markets. The effect is very stable, both across time and asset classes. It makes the existence of trends one of the most statistically significant anomalies in financial markets. When analyzing the trend following signal further, we find a clear saturation effect for large signals, suggesting that fundamentalist traders do not attempt to resist "weak trends", but step in when their own signal becomes strong enough. Finally, we study the performance of trend following in the recent period. We find no sign of a statistical degradation of long trends, whereas shorter trends have significantly withered.Comment: 17 pages, 9 figures, 9 table

Statistical properties of stock order books: empirical results and models

We investigate several statistical properties of the order book of three liquid stocks of the Paris Bourse. The results are to a large degree independent of the stock studied. The most interesting features concern (i) the statistics of incoming limit order prices, which follows a power-law around the current price with a diverging mean; and (ii) the humped shape of the average order book, which can be quantitatively reproduced using a `zero intelligence' numerical model, and qualitatively predicted using a simple approximation.Comment: Revised version, 10 pages, 4 .eps figures. to appear in Quantitative Financ

Emergence of time-horizon invariant correlation structure in financial returns by subtraction of the market mode

We investigate the emergence of a structure in the correlation matrix of assets' returns as the time-horizon over which returns are computed increases from the minutes to the daily scale. We analyze data from different stock markets (New York, Paris, London, Milano) and with different methods. Result crucially depends on whether the data is restricted to the ``internal'' dynamics of the market, where the ``center of mass'' motion (the market mode) is removed or not. If the market mode is not removed, we find that the structure emerges, as the time-horizon increases, from splitting a single large cluster. In NYSE we find that when the market mode is removed, the structure of correlation at the daily scale is already well defined at the 5 minutes time-horizon, and this structure accounts for 80 % of the classification of stocks in economic sectors. Similar results, though less sharp, are found for the other markets. We also find that the structure of correlations in the overnight returns is markedly different from that of intraday activity.Comment: 12 pages, 17 figure

Quenched complexity of the p-spin spherical spin-glass with external magnetic field

We consider the p-spin spherical spin-glass model in the presence of an external magnetic field as a general example of a mean-field system where a one step replica symmetry breaking (1-RSB) occurs. In this context we compute the complexity of the Thouless-Anderson-Palmer states, performing a quenched computation. We find what is the general connection between this method and the standard static 1-RSB one, formulating a clear mapping between the parameters used in the two different calculations. We also perform a dynamical analysis of the model, by which we confirm the validity of our results.Comment: RevTeX, 11 pages, including 2 EPS figure

Implementation of Telehealth in Radiation Oncology: Rapid Integration During COVID-19 and its Future Role in our Practice.

Introduction: The widespread coronavirus disease 2019 (COVID-19) has resulted in significant changes in care delivery among radiation oncology practices and demanded the rapid incorporation of telehealth. However, the impact of a large-scale transition to telehealth in radiation oncology on patient access to care and the viability of care delivery are largely unknown. In this manuscript, we review our implementation and report data on patient access to care and billing implications. As telehealth is likely to continue after COVID-19, we propose a radiation oncology-specific algorithm for telehealth. Material and Methods: In March 2020, our department began to use telehealth for all new consults, post-treatment encounters, and follow-up appointments. Billable encounters from January to April 2020 were reviewed and categorized into one of the following visit types: in-person, telephonic, or two-way audio-video. Logistic regression models tested whether visit type differed by patient age, income, or provider. Results: There was a 35% decrease in billable activity from January to April. In-person visits decreased from 100% to 21%. Sixty percent of telehealth appointments in April were performed with two-way audio-video, and 40% by telephonic only. In-person consultation visits were associated with higher billing codes compared to two-way audio-video telehealth visits (p Conclusions: Since the onset of COVID-19 pandemic, we were able to move the majority of patient visits to telehealth but observed inconsistent utilization of the audio-video telehealth platform. We present guidelines and quality metrics for incorporating telehealth in radiation oncology practice, based on type of encounter and disease subsite

A Model-Based Method for Assessment of Salivary Gland and Planning Target Volume Dosimetry in Volumetric-Modulated Arc Therapy Planning on Head-and-Neck Cancer.

This study examined the relationship of achievable mean dose and percent volumetric overlap of salivary gland with the planning target volume (PTV) in volumetric-modulated arc therapy (VMAT) plan in radiotherapy for a patient with head-and-neck cancer. The aim was to develop a model to predict the viability of planning objectives for both PTV coverage and organs-at-risk (OAR) sparing based on overlap volumes between PTVs and OARs, before the planning process. Forty patients with head-and-neck cancer were selected for this retrospective plan analysis. The patients were treated using 6 MV photons with 2-arc VMAT plan in prescriptions with simultaneous integrated boost in dose of 70 Gy, 63 Gy, and 58.1 Gy to primary tumor sites, high-risk nodal regions, and low-risk nodal regions, respectively, over 35 fractions. A VMAT plan was generated using Varian Eclipse (V13.6), in optimization with biological-based generalized equivalent uniform dose (gEUD) objective for OARs and targets. Target dose coverage

On the formal equivalence of the TAP and thermodynamic methods in the SK model

We revisit two classic Thouless-Anderson-Palmer (TAP) studies of the Sherrington-Kirkpatrick model [Bray A J and Moore M A 1980 J. Phys. C 13, L469; De Dominicis C and Young A P, 1983 J. Phys. A 16, 2063]. By using the Becchi-Rouet-Stora-Tyutin (BRST) supersymmetry, we prove the general equivalence of TAP and replica partition functions, and show that the annealed calculation of the TAP complexity is formally identical to the quenched thermodynamic calculation of the free energy at one step level of replica symmetry breaking. The complexity we obtain by means of the BRST symmetry turns out to be considerably smaller than the previous non-symmetric value.Comment: 17 pages, 3 figure

n- исчисление – реалистичная формализация класса переписывающих систем

Предложен новый формализм типизированного η-исчисления в качестве теоретической основы для по-строения специальных классов систем программирования на основе переписывающих правил. Форма-лизм использует упорядоченные неконфлюэнтные множества правил переписывания и взаимодействие с программным окружением, что позволяет расширить возможности программирования динамических приложений

Regularizing Portfolio Optimization

The optimization of large portfolios displays an inherent instability to estimation error. This poses a fundamental problem, because solutions that are not stable under sample fluctuations may look optimal for a given sample, but are, in effect, very far from optimal with respect to the average risk. In this paper, we approach the problem from the point of view of statistical learning theory. The occurrence of the instability is intimately related to over-fitting which can be avoided using known regularization methods. We show how regularized portfolio optimization with the expected shortfall as a risk measure is related to support vector regression. The budget constraint dictates a modification. We present the resulting optimization problem and discuss the solution. The L2 norm of the weight vector is used as a regularizer, which corresponds to a diversification "pressure". This means that diversification, besides counteracting downward fluctuations in some assets by upward fluctuations in others, is also crucial because it improves the stability of the solution. The approach we provide here allows for the simultaneous treatment of optimization and diversification in one framework that enables the investor to trade-off between the two, depending on the size of the available data set