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Remarks Before The 2017 AICPA National Conference On Current SEC And PCAOB Developments - Joseph R. Epstein, Professional Accounting Fellow, Office Of The Chief Accountant

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The Securities and Exchange Commission, as a matter of policy, disclaims responsibility for any private publication or statement by any of its employees. The views expressed herein are those of the author and do not necessarily reflect the views of the Commission or of the author’s colleagues upon the staff of the Commission.

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CME Group Chief Financial Officer, Global Head Of Financial And OTC Products And Senior Managing Director Of Government Relations And Regulatory Affairs To Present At Goldman Sachs Conference

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CME Group announced today that John Pietrowicz, Chief Financial Officer; Sean Tully, Senior Managing Director and Global Head of Financial and OTC Products; and Linda Rich, Senior Managing Director of Government Relations and Legislative Affairs, will present at the Goldman Sachs U.S. Financial Services Conference in New York on Tuesday, December 5, at 1:30 p.m. (Eastern Time). 

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Remarks Before The 2017 AICPA Conference On Current SEC And PCAOB Developments, Michael P. Berrigan, Professional Accounting Fellow, Office Of The Chief Accountant

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The Securities and Exchange Commission (“SEC” or “Commission”) disclaims responsibility for any private publication or statement of any SEC employee or Commissioner. This speech expresses the author's views and does not necessarily reflect those of the Commission, the Commissioners, or other members of the staff.

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Think Like a Traveler

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IDEO's Tom and Dave Kelley wrote a wonderful book together a few years ago titled, "Creative Confidence."   In the book, they offer tons of ideas on how to spark creativity in your organization, based mostly on their work developing design thinking as a process and set of tools for creating breakthrough innovations.  One of my favorite passages has to do with encouraging people to "think like a traveler."  Here is an excerpt:

Ever travel to a foreign city?  We've all heard that, "Travel broadens the mind."  But beneath this cliche lies a deep truth.  Things stand out becuase they're different, so we notice every detail, from street signs to mailboxes to how you pay at a restaurant.  We learn a lot when we travel not becuase we are any smarter on the road, but because we pay such close attention.  On a trip, we become our own version of Sherlock Holmes, intensely observing the environment around us.  We are continuously trying to figure out a world that is foreign and new. Too often, we go through day-to-day life on cruise control, oblivious to huge swaths of our surroundings.  To notice friction points - and therefore opportunities to do things better - it helps to see the world with fresh eyes.

Psychologists distinguish between two ways of perceiving the world around us and processing information - top down vs. bottom up processing.  In top down processing, we draw on our past experiences and "fill in the blanks" when we encounter a particular place or situation.   We don't have to notice every detail, because a few signs prove sufficient to let us know what we are seeing.  We walk into a library, and we know quickly based on a few visual cues that we are in a library.  We don't need to attend to all the details.   In bottom up processing, we start by perceiving all the little details, and we put the pieces of the puzzle together gradually.   In day-to-day life, as the Kelleys explain, we are on cruise control, using top down processing to capture the essence of a situation quickly and fill in the blanks to paint the picture in our mind.   When we travel, we engage in bottom up processing, because we can't rely on past experience to help us.   As such, we notice lots of little things.  Noticing the little opportunities for improvement and innovation can be crucial in the creative process.  Thus, thinking like a traveler is essential to creativity.  

CBOE to Begin Bitcoin Futures Trading December 10

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The Chicago Board Options Exchange (CBOE) has announced that its planned bitcoin futures product will begin trading on Dec. 10.

Blockchains Are Forever? Diamond Giant De Beers Unveils DLT Strategy

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One of the world's most well-known diamond companies is getting into blockchain by investing in an asset tracking platform.

Loveable Digital Kittens Are Clogging Ethereum's Blockchain

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Can a crypto app be too easy and fun? That might be the case on ethereum, where one project is proving so popular it's putting pressure on the network's technology. Best thought of as a decentralized Tamagotchi, CryptoKitties appears to be striking a nerve with new users, making ethereum fun and accessible to those who aren't in […]

Moscow Government Open-Sources Blockchain Voting Tool

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The government of Moscow is pushing ahead with plans to test blockchain for use in its municipal elections.

Alleged ICO Fraudster Pleads Not Guilty in New York Court

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A New York businessman charged with defrauding investors in two initial coin offerings (ICOs) plead not guilty in court last week.

Inferring agent objectives at different scales of a complex adaptive system. (arXiv:1712.01137v1 [q-fin.TR])

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We introduce a framework to study the effective objectives at different time scales of financial market microstructure. The financial market can be regarded as a complex adaptive system, where purposeful agents collectively and simultaneously create and perceive their environment as they interact with it. It has been suggested that multiple agent classes operate in this system, with a non-trivial hierarchy of top-down and bottom-up causation classes with different effective models governing each level. We conjecture that agent classes may in fact operate at different time scales and thus act differently in response to the same perceived market state. Given scale-specific temporal state trajectories and action sequences estimated from aggregate market behaviour, we use Inverse Reinforcement Learning to compute the effective reward function for the aggregate agent class at each scale, allowing us to assess the relative attractiveness of feature vectors across different scales. Differences in reward functions for feature vectors may indicate different objectives of market participants, which could assist in finding the scale boundary for agent classes. This has implications for learning algorithms operating in this domain.

A particle model for the herding phenomena induced by dynamic market signals. (arXiv:1712.01085v1 [q-fin.CP])

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In this paper, we study the herding phenomena in financial markets arising from the combined effect of (1) non-coordinated collective interactions between the market players and (2) concurrent reactions of market players to dynamic market signals. By interpreting the expected rate of return of an asset and the favorability on that asset as position and velocity in phase space, we construct an agent-based particle model for herding behavior in finance. We then define two types of herding functionals using this model, and show that they satisfy a Gronwall type estimate and a LaSalle type invariance property respectively, leading to the herding behavior of the market players. Various numerical tests are presented to numerically verify these results.

A Numerical Method for Pricing Discrete Double Barrier Option by Lagrange Interpolation on Jacobi Node. (arXiv:1712.01060v1 [q-fin.CP])

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In this paper, a rapid and high accurate numerical method for pricing discrete single and double barrier knock-out call options is presented. According to the well-known Black-Scholes framework, the price of option in each monitoring date could be calculate by computing a recursive integral formula upon the heat equation solution. We have approximated these recursive solutions with the aim of Lagrange interpolation on Jacobi polynomials node. After that, an operational matrix, that makes our computation significantly fast, has been driven. The most important feature of this method is that its CPU time dose not increase when the number of monitoring dates increases. The numerical results confirm the accuracy and efficiency of the presented numerical algorithm.

The balance of growth and risk in population dynamics. (arXiv:1712.00979v1 [cond-mat.stat-mech])

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Essential to each other, growth and exploration are jointly observed in populations, be it alive such as animals and cells or inanimate such as goods and money. But their ability to move, crucial to cope with uncertainty and optimize returns, is tempered by the space/time properties of the environment. We investigate how the environment shape optimal growth and population distribution in such conditions. We uncover a trade-off between risks and returns by revisiting a common growth model over general graphs. Our results reveal a rich and nuanced picture: fruitful strategies commonly lead to risky positions, but this tension may nonetheless be alleviated by the geometry of the explored space. The applicability of our conclusions is subsequently illustrated over an empirical study of financial data.

Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis. (arXiv:1712.00975v1 [cs.CE])

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Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale Limit Order Book (LOB) dataset show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations.

An Inverse Problem Study: Credit Risk Ratings as a Determinant of Corporate Governance and Capital Structure in Emerging Markets: Evidence from Chinese Listed Companies. (arXiv:1712.00602v1 [q-fin.EC])

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Credit risk rating is shown to be a relevant determinant in order to estimate good corporate governance and to self-optimize capital structure. The conclusion is argued from a study on a selected (and justified) sample of (182) companies listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange and which use the same Shanghai Brilliance Credit Rating & Investors Service Company assessment criteria, for their credit ratings, from 2010 to 2015. Practically, 3 debt ratios are examined in terms of 11 characteristic variables. Moreover, any relationship between credit rating and corporate governance can be thought to be an interesting finding. The relationship between credit rating and leverage is not as evident as that found by other researchers from different countries; it is significantly positively related to the outside director, firm size, tangible assets and firm age, and CEO and chairman office plurality. However, leverage is found to be negatively correlated with board size, profitability, growth opportunity, and non-debt tax shield. Credit rating is positively associated with leverage, but in a less significant way. CEO-Board chairship duality is insignificantly related to leverage. The non-debt tax shield is significantly correlated with leverage. The correlation coefficient between CEO duality and auditor is positive but weakly significant, but seems not consistent with expectations. Finally, profitability cause could be regarded as an interesting finding. Indeed, there is an inverse correlation between profitability and total debt (Notice that the result supports the pecking order theory). In conclusion, it appears that credit rating has less effect on the so listed large Chinese companies than in other countries. Nevertheless, the perspective of assessing credit risk rating by relevant agencies is indubitably a recommended time dependent leverage determinant.


Dynamic optimization of a portfolio. (arXiv:1712.00585v1 [q-fin.PM])

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In this paper, we consider the problem of optimization of a portfolio consisting of securities. An investor with an initial capital, is interested in constructing a portfolio of securities. If the prices of securities change, the investor shall decide on reallocation of the portfolio. At each moment of time, the prices of securities change and the investor is interested in constructing a dynamic portfolio of securities. The investor wishes to maximize the value of his portfolio at the end of time $T$. We use a novel theoretical approach based on dynamic programming to solve the age old problem of dynamic programming. We consider two cases i.e. Deterministic and Stochastic to approach the problem and show how the portfolio is maximized using dynamic programming.

A Neural Stochastic Volatility Model. (arXiv:1712.00504v1 [cs.LG])

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In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms stronge baseline methods, including the deterministic models, such as GARCH and its variants, and the stochastic MCMC-based models, and the Gaussian-process-based, on the average negative log-likelihood measure.

Retirement Wealth under Fixed Limits: The Optimal Strategy for Exponential Utility. (arXiv:1712.00463v1 [q-fin.PM])

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For an exponential utility maximizing investment strategy in a Black-Scholes Setting, fixed upper and lower constraints are introduced on the terminal wealth. This is equivalent to combining the optimal strategy with options. The resulting distribution is investigated in terms of change of quantiles. The theory is illustrated with quantitative examples, including an assessment of the effects of restricting the strategy to positive investments.

Savvy Investor Awards 2017: The Best White Papers

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The best research papers of 2017. (via @savvyinv) https://t.co/9dDL3VzD94 — Tadas Viskanta (@abnormalreturns) December 5, 2017

LCH Clears Over $700 Million In G10 NDF In One Month

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LCH, a leading global clearing house, today announced that it has cleared a total of $745 million G10 FX NDFs currency pairs through its ForexClear service. This volume was achieved within one month of the new product’s go live at LCH. By clearing these additional currency pairs at ForexClear, market participants will benefit from the efficiencies of clearing over a broader product set.

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