### Headlines:

How to Create an ARIMA Model for Time Series Forecasting with Python
(machinelearningmastery.com)
The relationship between trading frequency and achievable alpha
(automatedtrader.net)
‘Crowdsourced’ Quantopian dives into algorithms
(ft.com)
Quant ‘Alpha Factory’ Hunts for Investing Edge
(wsj.com)
Hedge Funds v. Silicon Valley, The Battle For Quant Talent
(worldcrunch.com)

### Trending academic research:

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Most popular SSRN papers
The Magic Formula: Value, Profitability, and the Cross Section of Global Stock Returns
A level-1 Limit Order book with time dependent arrival rates
abstract We propose a simple stochastic model for the dynamics of a limit order book, extending the recent work of Cont and de Larrard (2013), where the price dynamics are endogenous, resulting from market transactions. We also show that the conditional diffusion limit of the price process is the so-called Brownian meander.
Fast Quantization of Stochastic Volatility Models
abstract Recursive Marginal Quantization (RMQ) allows fast approximation of solutions to stochastic differential equations in one-dimension. When applied to two factor models, RMQ is inefficient due to the fact that the optimization problem is usually performed using stochastic methods, e.g., Lloyd's algorithm or Competitive Learning Vector Quantization. In this paper, a new algorithm is proposed that allows RMQ to be applied to two-factor stochastic volatility models, which retains the efficiency of gradient-descent techniques. By margining over potential realizations of the volatility process, a significant decrease in computational effort is achieved when compared to current quantization methods. Additionally, techniques for modelling the correct zero-boundary behaviour are used to allow the new algorithm to be applied to cases where the previous methods would fail. The proposed technique is illustrated for European options on the Heston and Stein-Stein models, while a more thorough application is considered in the case of the popular SABR model, where various exotic options are also priced.
On mean-variance hedging under partial observations and terminal wealth constraints
abstract In the paper, a mean-square minimization problem under terminal wealth constraint with partial observations is studied. The problem is naturally connected to the mean-variance hedging problem under incomplete information. A new approach to solving this problem is proposed. The paper provides a solution when the underlying pricing process is a square-integrable semimartingale. The proposed method for the study is based on the martingale representation. In special cases, the Clark-Ocone representation can be used to obtain explicit solutions. The results and the method are illustrated and supported by example with two correlated geometric Brownian motions.
Modern Portfolio Theory, Digital Portfolio Theory and Intertemporal Portfolio Choice
Introducing Global Term Structure in a Risk Parity Framework
Scaling evidence of the homothetic nature of cities
abstract In this paper we analyse the profile of land use and population density with respect to the distance to the city centre for the European city. In addition to providing the radial population density and soil-sealing profiles for a large set of cities, we demonstrate a remarkable constancy of the profiles across city size.

Our analysis combines the GMES/Copernicus Urban Atlas 2006 land use database at 5m resolution for 300 European cities with more than 100.000 inhabitants and the Geostat population grid at 1km resolution. Population is allocated proportionally to surface and weighted by soil sealing and density classes of the Urban Atlas. We analyse the profile of each artificial land use and population with distance to the town hall.

In line with earlier literature, we confirm the strong monocentricity of the European city and the negative exponential curve for population density. Moreover, we find that land use curves, in particular the share of housing and roads, scale along the two horizontal dimensions with the square root of city population, while population curves scale in three dimensions with the cubic root of city population. In short, European cities of different sizes are homothetic in terms of land use and population density. While earlier literature documented the scaling of average densities (total surface and population) with city size, we document the scaling of the whole radial distance profile with city size, thus liaising intra-urban radial analysis and systems of cities. In addition to providing a new empirical view of the European city, our scaling offers a set of practical and coherent definitions of a city, independent of its population, from which we can re-question urban scaling laws and Zipf's law for cities.
Enhancing Enterprise Value by Trading Options
Pairs Trading Under Drift Uncertainty and Risk Penalization
Estimating Robustness
The Cross-Section of Government Bond Returns
Stock Market Co-Movement at the Disaggregated Level: Individual Stock Integration
Endogenous and Exogenous Risk Premia
Thinking About Theta: An Analysis of Time Decay, Early Unwind and Closeout Feedback Effects
The Skewness and Kurtosis of European Options and the Implications for Trade Sizing
A generalized Bayesian framework for the analysis of subscription based businesses
abstract We have created a framework for analyzing subscription based businesses in terms of a unified metric which we call SCV (single customer value). The major advance in this paper is to model customer churn as an exponential decay variable, which directly follows from experimental data relating to subscription based businesses. This Bayesian probabilistic model was used to compute an expected value for the revenue contribution of a single user. We obtain an exact closed-form solution for the constant churn model, and an approximate closed-form solution for the exponential decay model. In addition, we define a general methodology for decision making processes using sensitivity analysis of the model equation, which we illustrate with a real-life case study for a food based subscription business.
Trading Fees and Intermarket Competition
Anomalous Scaling of Stochastic Processes and the Moses Effect
abstract The state of a stochastic process evolving over a time $t$ is typically assumed to lie on a normal distribution whose width scales like $t^{1/2}$. However, processes where the probability distribution is not normal and the scaling exponent differs from $\frac{1}{2}$ are known. The search for possible origins of such "anomalous" scaling and approaches to quantify them are the motivations for the work reported here. In processes with stationary increments, where the stochastic process is time-independent, auto-correlations between increments and infinite variance of increments can cause anomalous scaling. These sources have been referred to as the $\it{Joseph}$ $\it{effect}$ the $\it{Noah}$ $\it{effect}$, respectively. If the increments are non-stationary, then scaling of increments with $t$ can also lead to anomalous scaling, a mechanism we refer to as the $\it{Moses}$ $\it{effect}$. Scaling exponents quantifying the three effects are defined and related to the Hurst exponent that characterizes the overall scaling of the stochastic process. Methods of time series analysis that enable accurate independent measurement of each exponent are presented. Simple stochastic processes are used to illustrate each effect. Intraday Financial time series data is analyzed, revealing that its anomalous scaling is due only to the Moses effect. In the context of financial market data, we reiterate that the Joseph exponent, not the Hurst exponent, is the appropriate measure to test the efficient market hypothesis.
A Fuzzy Approach for Expert Evaluation of Investment Portfolios
From Failure to Success: Replacing the Failure Rate
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