A Black-Litterman portfolio allocation model combined with a Markov switching framework

This is a M.Sc. thesis investigating the compatibility and performance of a regime switching framework as a complement to the Black-Litterman portfolio allocation model. Conclusively, it is considered to be a compatible match of models in terms of practical implementation and the results indicate that the model is performing well.Portfolio allocation using short term predictions of business cycles The Black-Litterman model is an asset allocation model developed by Fischer Black andRobert Litterman in the early 90’s. It was published in the internal Goldman Sachs Fixed Income Research Note, Black and Litterman (1990). The model is an advanced Mean Variance Optimization framework, with an objective to maximize the return of a portfolio in relation to its risk. The primary reason for using the Black-Litterman model instead of a traditional Mean Variance Optimization model is to overcome problems such as unintuitive, highly concentrated portfolios, input sensitivity, and estimation er-ror maximization. This is achieved by incorporating subjective investor opinions, called views, in the model, which usually works fine if the views are appropriately formulated. The problem with this concept is that it is not very easy to achieve consistency and operational efficiency when specifying the views. This thesis examines a model using historical data to find trends in the business cycle as a tool to extract the views. The model used is a regime switching model and more specifically a Markov switching model. There are two reasons for choosing a model using historical data when creating views. Firstly, it is less time consuming to implement a model that automatically turns public information into views. Secondly, it formulates every view from a precise framework and is therefore more consistent in its allocation. However, the Black-Litterman model is successful because of its ability to use additional information not found in the historical exchange databases. This raises the question whether a method that is generating views from market data will enhance the performance or not. Someone once said investing this way is like “driving a car looking in the rear mirror”. The response to people distrusting the concept is that a regime switching model can spot trends which are assumed to be related to market consensus and indirectly to the investor opinions. In financial modelling a portfolio return series can traditionally be assumed to have a constant mean and a constant variance over a certain period of time. A two state Markov switching model instead calculates the probability for the portfolio to be in certain states during different stages of the same period. The model does this by assuming that the states can be described by a hidden Markov chain. The parameters of the hidden Markov chain cannot be observed and must be estimated from the market data. Each state has different statistical parameters, such as mean and variance. A high variance combined with a low mean could indicate that bear market conditions are dominating and vice versa could indicate a bull market. This market insight will be used to assign views. In this thesis a model combining the Markov switching and Black-Litterman models is used to repeatedly reallocate a portfolio with 10 stocks listed on the OMXS30 during the period August 2014 to October 2017. Four reallocations are made and the result is compared to a market weighted portfolio. A positive conclusion is that the two frame-works are practically compatible and that the new portfolio outperforms the traditional market portfolio in terms of absolute return and Sharpe ratio. The challenge lies in refining the Markov Switching model in order to let it handle larger data sets. It would be beneficial to test the entire model with different market conditions and other assets before it can be considered a reliable investment tool

Staging EUrope

"We get to the Lungotevere Papereschi late, but this is Rome and we know the spettacolo will not begin on time. If we were going to the theatre in New York, London or Munich, well then. Still we walk quickly past the beautifully lit outdoor space that announces the theatre even before one is in it, the soft candles on the ruined wall, the milling about a makeshift bar. We round the corner of the theatre where there is a huge queue pressed against a side of the building not customarily used as an entrance.

Itinerant Spectator/Itinerant Spectacle

Itinerant Spectator/Itinerant Spectacle moves across the landscape of European performance in late 20th and early 21st centuries, recounting performance in circulation across national borders and across the itinerant bodies of spectators who travel to meet performances that travel. Itinerant Spectator/Itinerant Spectacle suggests spectating is a practice — an act of interpretation engaged in more than simply receiving the affects of a performance, a companion practice to the making of performance. The work forms a part of Skantze’s ongoing explorations of what she terms the ‘epistemology of practice as research.’ IS/IS theorizes spectating as a practice that extends beyond the theatre, as a practice of writing as recollecting (and recollecting as writing) at the center of what has been called “criticism.” The book grounds spectatorship in the subjective, embodied, differenced practice of spectating not from a fixed location or standpoint but from a ground that constantly shifts, that is, from the ground of the roving positionalities of the “itinerate spectator.” Following Walter Benjamin, for example, Skantze importantly adopts the privileges of the flaneur as a feminist and rather queer project, one that refuses to be tied to the minor position, to that of the impossible “flaneuse.

TurnGPT: a Transformer-based Language Model for Predicting Turn-taking in Spoken Dialog

Syntactic and pragmatic completeness is known to be important for turn-taking prediction, but so far machine learning models of turn-taking have used such linguistic information in a limited way. In this paper, we introduce TurnGPT, a transformer-based language model for predicting turn-shifts in spoken dialog. The model has been trained and evaluated on a variety of written and spoken dialog datasets. We show that the model outperforms two baselines used in prior work. We also report on an ablation study, as well as attention and gradient analyses, which show that the model is able to utilize the dialog context and pragmatic completeness for turn-taking prediction. Finally, we explore the model's potential in not only detecting, but also projecting, turn-completions.Comment: Accepted to Findings of ACL: EMNLP 202

A General, Abstract Model of Incremental Dialogue Processing

We present a general model and conceptual framework for specifying architectures for incremental processing in dialogue systems, in particular with respect to the topology of the network of modules that make up the system, the way information flows through this network, how information increments are ‘packaged’, and how these increments are processed by the modules. This model enables the precise specification of incremental systems and hence facilitates detailed comparisons between systems, as well as giving guidance on designing new systems. In particular, the model can serve as a framework for specifying module communication in such systems, as we illustrate with some examples

The Open-domain Paradox for Chatbots: Common Ground as the Basis for Human-like Dialogue

There is a surge in interest in the development of open-domain chatbots, driven by the recent advancements of large language models. The "openness" of the dialogue is expected to be maximized by providing minimal information to the users about the common ground they can expect, including the presumed joint activity. However, evidence suggests that the effect is the opposite. Asking users to "just chat about anything" results in a very narrow form of dialogue, which we refer to as the "open-domain paradox". In this position paper, we explain this paradox through the theory of common ground as the basis for human-like communication. Furthermore, we question the assumptions behind open-domain chatbots and identify paths forward for enabling common ground in human-computer dialogue.Comment: Accepted at SIGDIAL 202