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Triple exponential smoothing for equity instruments - also known as the Winters method - is a refinement of the popular double exponential smoothing model with the addition of periodicity (seasonality) component. Simple exponential smoothing technique works best with data where there are no trend or seasonality components to the data. When equity instruments prices exhibit either an increasing or decreasing trend over time, simple exponential smoothing forecasts tend to lag behind observations. Double exponential smoothing is designed to address this type of data series by taking into account any trend in equity instruments price movement. However, neither of these exponential smoothing models address any seasonality of ###3###.
As with simple exponential smoothing, in triple exponential smoothing models past equity instruments observations are given exponentially smaller weights as the observations get older. In other words, recent observations are given relatively more weight in forecasting than the older price observations.
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The classical approach to portfolio optimization is known as Modern Portfolio Theory (MPT). It involves categorizing the investment universe based on risk (standard deviation) and return, and then choosing the mix of investments that achieves the desired risk-versus-return tradeoff. Portfolio optimization can also be thought of as a risk-management strategy as every type of equity has a distinct return and risk characteristics as well as different systemic risks, which describes how they respond to the market at large. Macroaxis enables investors to optimize portfolios that have a mix of equities (such as stocks, funds, or ETFs) and cryptocurrencies (such as Bitcoin, Ethereum or Monero)
Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Analyst Advice module to analyst recommendations and target price estimates broken down by several categories.
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