played throughout the series by Keanu Reeves) in a psychological therapy session. Anderson t...I would say that you can’t make up this sh*t, but over the years, horror movie writers and directors have created some pretty crazy creatures.
Ranging from the silly to the disgusting, here are 12 of the most bizarre movie monsters ever depicted on film.
Attack of the Killer Tomatoes (1978)
In this spoof of horror B-movies, tomatoes become sentient for unknown reasons and attack humanity. And it actually kind of works.
Here, take a look at the trailer for the original film.
Hey, I said we were starting this list with the silliest! 😉. A zombie, venus fly-trap? I’m listening…. First killer tomatoes, now a killer venus fly-trap. Weird.well Clothing, an offspring of the screen printing company, The Prince Ink located in the Phoebus area of Hampton, seeks to pay homage to an earlier time in America when hand made high quality products were a common theme. Every single design was drawn by hand, printed by hand (with hand mixed inks), washed by hand, and custom packaged by hand.
Fairwell Clothing is more than pleased and excited to launch the ³Seven Cities Series² line of shirts and posters. The ³Seven Cities Series² is inspired by the rich history of Hampton Roads, dedicating a design to each of the seven cities with historic relevance to each.
*Clothing can be purchased online or in store (Mellen StreetWhy is nobody asking... what is the interest rate on the debt?

If it's 5-7%, hang onto the 10K in savings (babies are expensive) and just make larger payments.

If it's 7+%, then I support a bulk payment on the debt.

IMHO you're not in the right place financially for a home, and that debt will count against you anyway.

It also matters - where did the $10K come from? Was it from a windfall, or did you painstakingly save it up? Over how long? Can you keep saving at that rate?

I will repeat, babies are expensive. Never underestimate that part.  stripy canvas fabric with dark green lining and red trim.
Description
A fully lined peg bag carefully made using a sturdy canvas style fabric with cream, beige and white stripes. The back is lined with a dark green fabric printed with tiny apples and stars and you can see this through the opening. The front is lined with beige cotton fabric.
I have made bias binding from red and white checked cotton fabric to bind the opening, I think this gives it a bit of country style..The academic credibility of Alternative Risk Premia (ARP) strategies is as important as their reliance on data and computer science to define their harvesting algorithms. In combination, these traits have created what we regard as an erroneous impression that ARP approaches are constructed from generic building blocks readily available to every investor in the market and that ARP, as an industry, is destined to become commoditised. This conclusion has been reinforced by the use of similar categorisations by asset class (foreign exchange, equities, interest rates, commodities, volatility) and by style (momentum, value, carry) – in other words, a shared risk premia phrasebook. We firmly believe that the opportunity set offered by ARP is essentially heterogeneous, at both the strategy and the aggregated portfolio level.
This lack of homogeneity is clearly demonstrated by the significantly different results, in terms of long-term performance, yielded across the ARP universe. For instance, the expected drawdown analysis favoured by GAM Systematic tends to be more counter-cyclical than methodologies based on risk parity and realised volatility, which tend to have higher weights in strategies that have recently shown strong performance. Even more importantly, however, we believe that the process of successfully harvesting single risk premia requires years of experience and constant attention to detail. For instance, it may appear easy to employ generic momentum models to extract risk premia from the foreign exchange markets. In reality, it is indeed complex to decide which specific contracts to consider and how to integrate them into an actual strategy.
Our long experience in the ARP space has clearly shown to us that an integrated process, whereby the ARP investment manager fully understands and controls the harvesting algorithms and their implementation, as well as the overall risk exposures within his or her portfolio, is an indispensable precondition for rewarding investment results. Properly managed, we believe the eventual outcome of such an approach will be outperformance.
In this article, we focus on the heterogeneity in ARP performance and illustrate possible sources of dispersion among risk premia harvesting algorithms. For illustrative purposes, we consider how a relatively straightforward (at first sight) foreign exchange trend model strategy could be designed. Such a model aims to capture the momentum risk premium in currency markets by dynamically switching between long and short exposure to the underlying currencies based on the observation of current versus historical prices. First discussed in equity markets (before FX rates were allowed to float in 1973), the momentum risk premium is well known and robust. It has been shown to exist across a broad range of markets and asset classes (Asness et al (2013)). Currency momentum has specifically been documented by Okunev and White (2003).
There are various explanations for the momentum effect and these are based on either behavioural finance or rational expectation assumptions. The former evidences herding1, investor over and under-reaction and the confirmation bias2, while the latter stresses exposure to global political and economic risks, and to hedging pressures (Filippou et al (2018)). Irrespective of the rationale behind it, the foreign exchange trend strategy is considered simple because its investable universe is relatively contained, while the individual instruments are very liquid. Transaction costs are low and there are no short-selling constraints. Common momentum signals have been widely employed as well as examined in academic literature (eg Guilleminot et al (2013)). As a result, we believe they can hardly be regarded as stemming from the special, obscure knowledge of certain opaque hedge funds. Finally, the pertinent data can be easily sourced and a basic algorithm requires limited programming skills and computational power.
This, or in fact any systematic investment strategy, can be represented by means of the five blocks or steps, as shown in Exhibit 1. Any systematic investment manager will, in principle, rely on such a structure.
In particular, anyone designing an ARP model must make specific decisions on how to complete each of these boxes. These differing decisions will impact strategy characteristics, thus creating dispersion among investment managers harvesting the same risk premium. While outperformance is often explained by referencing the step involving the signals, it should be stressed that each step is important and will contribute to the final result. Exhibit 2 shows sample questions that can be addressed at each of the presented steps, as well as possible responses.
Source: GAM Systematic. For illustrative purposes only.
Source: GAM Systematic. For illustrative purposes only. Allocations and holdings are subject to change.
The specific choices made and parameters chosen will differentiate investment managers and product solutions. Some choices generate relatively minor differences, while others may have a substantial impact on overall exposure, risk profile and thus performance.
To illustrate these general points, we constructed a set of foreign exchange trend-following strategies. Exhibit 3 shows simulated Sharpe ratios obtained by running such models with different choices of parameters over almost two decades, from January 2000 to June 2019. These parameters comprise: the investable universe; a set of different momentum signals (for different time frames); and a set of methodologies to weight positions. For each run, we choose a set of contracts, pick one signal and a methodology to weight positions. The Sharpe ratios obtained by even this simple simulation differ widely, spanning a range from 0 to 0.6, averaging around 0.3, the expected level for such a strategy.
Source: GAM Systematic. For illustrative purposes only. Simulations are run over the period January 2000 – June 2019.
The key questions are: how do we ensure choices are robust and optimally deliver on the model’s premise to harvest the respective risk premium? And what are the investment rationales, the expected features of the model and our overall modus operandi?
In-sample performance should clearly not be a driver of these choices, as this usually leads to what is known as ‘over-fitting’ (a modelling error that occurs when a function is too closely fitted to a limited set of data points) and poor out-of-sample performance. For the sake of strong in-sample performance, the number of parameters and choices to be made is often large. In most modelling frameworks, it is always possible to find the perfect back-test. As we do not focus on the past performance of investment strategies, what do we focus the modelling attention on instead? We aim to analyse the strategies’ risks and exposures, as well as to understand their behaviour and the performance drivers. Of course, having a long experience in harvesting risk premia is of great benefit in this process.
When an investment strategy is tested, based on a certain choice of parameters, the period over which it is tested is called the in-sample period. The in-sample period might be the entire history of data available to run the strategy on or parts of that historical dataset. The resulting properties of the strategy, eg performance and risk features, are then called in-sample properties. The out-of-sample period is basically the dataset on which the investment strategy was not tested. In particular, since the strategy can only be tested on past data, the out-of-sample period contains all future data points. Any investment strategy seeks to deliver good risk-adjusted performance. Hence, risk strategies tend to display good performance referencing the in-sample period