Some of functionality may not work while you disabled JavaScript. Enable JavaScript for better User Exprience. October 30, 2017

LPs Talk Quants, Pros (& Cons) Of Seeding New Managers

A structural shift is happening in how institutional investors approach hedge funds. Technology plays an increasingly important role in hedged strategies, and institutions are looking to newer, hungrier managers for long-term partnership. Panelists include: Jonathan Hook, chief investment officer, Harry and Janet Weinberg Foundation; Pamela Campbell, managing director of hedged strategies, Washington University in St. Louis; Dan Parker, deputy chief investment officer, Texas Tech University. Elaine Orr moderated.
Read by 73% of LPs November 02, 2016

Linear Talk: Macromoney - The equities oriented Hedge Fund

Simone Dalle Nogare - Director at Macromoney Global Investments, joins Colin Lloyd, Author of Macro Investment Letter Service 'In the Long run', in the Tip TV - Linear Talk Hedge Show, and details why they run a Hedge Fund which bets only on equities, and how their macroeconomic model helps them identify the best bet in equities in this era of uncertainty. Nogare also offers inputs on their team, and their strategy for investing using derivatives.
Read by 58% of LPs June 30, 2016

Some Emerging Hedge Fund Managers Reactions To Brexit - Mr. Simone Dalle Nogare

"Some emerging hedge fund managers’ reactions to Brexit" Opalesque interviews Macromoney Director, Simone Dalle Nogare Mr. Simone Dalle Nogare, director at MacroMoney, said his global macro fund does not forecast but rather reacts to the speed of change (acceleration, deceleration, contraction) of the macroeconomic indicators. "Taking into consideration our approach," he told Opalesque, "I can tell you that at the moment our proprietary macroeconomic model is showing a deceleration of the macroeconomic indicators since one year ago. This deceleration, together with a strong event like Brexit, could push the world economy into recession. We will be closely following the economic indicators in the next two to three months to see if this scenario materializes. If this is the case, then we expect in the medium term a downtrend in the equity markets.” Macromoney Global Investments is a global macrofund with an equity bias domiciled in the BVIs, with $9m in assets under management (AuM). It is up 5.9% YTD (to end May), and the cumulative return since its January 2013 inception is 108% (compared to 47% for the S&P 500). The HFRX Macro Index, which includes both fundamental and quant CTA strategies, posted a decline of -0.55% on Friday (-2% YTD), with contribution from the positioning in the pound, yen, euro, gold, global equities and fixed income.
Read by 41% of LPs June 09, 2016

Embracing Macroeconomic Indicators

How macroeconomic data can drive asset allocation decisions By Maciej Wisniewski, Piotr Kawala and Radek Pszczółkowski, Macromoney It is often easy to assume that macroeconomic indicators are not helpful in predicting the course of markets – the stock market, so the argument goes, is a better barometer. However, research can show that these factors move side by side and can be used to inform the asset allocation decisions a fund takes. At Macromoney we conducted an exercise in which we allocated assets between S&P 500 equities and US Treasury bonds, based on the acceleration or deceleration of the ISM index, a survey conducted by the Institute of Supply Management. We bought the S&P 500 when the ISM increased against the previous month (e.g. from 50.4 to 50.7), and bought Treasury bonds when the ISM fell against the previous month (e.g. from 50.7 to 50.6). We reallocated capital at the beginning of each month using back-tested ISM results, assuming foreknowledge of the ISM results. This produced a return that beat US bonds and the S&P 500 over a 1978-2015 time period scenario by more than 4.5 fold [see graph]. Hence, we have built a proprietary asset allocation model that is informed by macroeconomic indicators, helping us to identify the different stages of the economic cycle that will in turn indicate which asset class should be allocated to. Our model draws on a number of momentum signals: • While a standard momentum strategy is based on the prices of different asset classes, our signals use the momentum of macroeconomic indicators; • Our model draws on key macroeconomic data, including GDP, interest rates, inflation, unemployment data, the ISM index, industrial production and retail sales. • We also draw on other sources, like Federal Reserve Economic Data (FRED) from the St Louis Fed; • The length of the time series since 1978 is also important; • Finally, we look at investment decisions based on forecasts from the above mentioned economic data and the Model Vector Autoregression (VAR) in the statistical program. The results are used to determine the optimal choice of asset for each stage of the business cycle. It is important to emphasise that the model is not trying to forecast future economic indicators but instead is intended to monitor the change of speed of the key indicators: when these data are accelerating, a buy signal is generated, when they are decelerating a neutral signal is generated, and a sell signal occurs from contracting data. Allocation between asset classes Using signals generated from the model allows funds to be allocated between different asset classes. For buy signals most of the assets are allocated to a carefully selected equity portfolio. The fund is not allowed to allocate net short positions in the equity portfolio, and maintains an average equity exposure of between 50-120% of NAV. The portfolio is periodically hedged using volatility futures and options with an exposure of up to 20% of the NAV. For neutral signals the fund is restricted to no more than 100% of NAV in net equities exposure, with average equity exposure of between 0-50% and with the fixed income component averaging between 0-100%. Again, the portfolio is hedged periodically with volatility futures and options, with exposure up to 20% of NAV. Net short positions are taken in stock indexes and up to 60% of the portfolio can be composed of short positions in single name stocks. For sell signals the fund no longer can maintain net long positions in equities, and instead maintains a 50-100% weighting in fixed income government bonds. It may implement volatility futures and options strategies with exposure of up to 20% of NAV. Net short positions in stock exchange indexes and specific stocks can account for up to 100% of NAV. The investment process revolves around which signal has been generated: the model performs the role of an internal benchmark against which the fund is measured on an annual basis. This also represents the target the management team needs to beat. In practical terms, using real market scenarios, the long term macroeconomic model generated a buy signal in the first quarter of 2015. From April slowing macroeconomic data signalled a retreat from equities. At the same time, our short term model, which tracks volatility, indicated in July that there was a potential upcoming – and significant – spike in volatility. In response to these signals, we opened out of the money put options on the S&P 500.The initial total position in options in July was only 1.5% of NAV, but it generated a 15% net return in August. In conclusion our model looks at the economy and at capital markets from the perspective of the mid-term business cycles. This attitude helps not only to grow capital during a bull market but also to preserve capital in downturns. While in a long term uptrend the model results are in line with the S&P500, its strength is shown in a full cycle and especially during prolonged market contraction. The results of the fund itself, which are a combination of macroeconomic model signals and alpha generated by Macromoney’s investment team, show that it is possible to outperform the stock market in the long term with a disciplined and repeatable investment process backed by thorough research. Maciej Wisniewski is the fund manager of Macromoney Global Investments Ltd, a BVI based global macro fund with equity bias. Maciej has 20 years of experience in investments management and in successfully setting up investment funds. Maciej holds a Master's degree in Finance from London Business School and a second Master's in Finance and Banking from Warsaw School of Economics. Piotr Kawala and Radek Pszczółkowski are analysts at Macromoney. Previously they worked for leading Polish financial institutions. Piotr is also a lecturer at the Warsaw School of Economics.
Read by 64% of LPs January 26, 2016

Correction in USA, bear market on the Warsaw Stock Exchange - Macromoney Global Investments Ltd. - Harvest

Article on the Polish business press, Parkiet, written by the Macromoney analysts
Read by 53% of LPs October 26, 2015

Macromoney fund manager pitching the fund at the Hedge Fund Startup Forum in Zurich

Maciej Wisniewski, Macromoney fund manager, was pitching the fund at the Hedge Fund Startup Forum in Zurich on Wednesday 21st October 2015. The IIR’s Hedge Fund Startup Forum at The Dolder Grand Hotel in the heart of Zurich was moderated by prominent industry figures and it is considered to be the leading event for those looking to start a hedge fund. Last year over 200 emerging and startup managers and investors attended the conference.
Read by 56% of LPs