Forecasting The Markets by Managing the Data Monster

Accurate forecasting data is paramount to successfully trending the market and creating strategic advantages. A paper by Bill Stringer discusses the use of big data programs and how this influences the success of the chemical industry. For the purposes of my own research, I am considering the merits of big data in proper market forecasting. 

Big data can uncover information that is not easy to find or discern. The relationship between the data pieces offers an opportunity to find trends and information that is not contained in standalone measurements.  When organizations use big data well they can more accurately assess performance and variability to meet market trends. 

The author believes that big data is the next frontier of nearly every economic sector. Companies will find ways to appropriately analyze large and complex data sets in both the private and public arenas. The ability to harness the data monster will allow for greater waves of growth, productivity and innovation. 

Most of the world’s electronic data has been generated in the past few years and executives have yet to come to grips with the virtual world. As organizations get better at analytics they will be able to make more informed decisions. Using data to create better pricing strategies helps in providing greater profitability.

Market forecasting is complex and often relies on experience and the overall feeling of the analyst. This makes traditional methods relatively inaccurate. Larger data can afford the possibility of finding future demand and trends in order to encourage higher levels of organizational growth. 

The paper targets Dow as a primary big data leader. In fostering their growth they recruited 10 PhDs in computer sciences and supported them by a team of advanced analytic experts that mesh their skills with a business intelligence team. Their success is based on a number of important factors:

-Improved accuracy of forecasting. 

-Early indications of targets to correct actions.

-Cost and exchange rate analysis that offers better purchasing of materials.

-Greater staffing abilities that offer better skill levels at the times they are needed. 

The author indicates that data can be drawn from a number of important sources that include social networking and publically available data. Companies can use this data to make a better environmental scan and then incorporate that information to encourage stronger decision making. As companies improve in their ability to forecast the market, they will be able to stay ahead of market trends. 

The report highlights a few important issues. If we think about how public information encourages or discourages business decisions we will find that in general available pubic data is helpful. The problem is that many cities and states don’t have enough information to encourage investment and reduce risk. Offering better data, even if not analyzed, will allow for greater transparency as well as resources for public consumption for strategic decision making. 

Philinkiene, V. (2008). Competitive market demand: the conception and types in the context of forecasting. Economics & Management.

Successful Economic Forecasting with the Bayesian Method

Gupta and Kabundi (2010) started with an interesting question on which macroeconomic models are most likely to predict economic growth and success. Decision-makers that have tools are better able to make current decisions that are likely to foster greater growth in the future. The researcher used emerging markets of South Africa but these same models may apply to economic hubs and the factors that predict their success. 

Models are simply explanations that attempt to predict activities within the environment. Some models are more successful than others. Success is determined through a process of validity where multiple researchers over a period of time analyze the same phenomenon over and over in multiple ways to determine if the model makes sense. 

Common data points in measuring economic development include per capita growth rate, consumer price index (CPI), inflation, the money market rate, and the growth rate of nominal effective exchange rates. These data points often work their way into various models in an effort to create and develop some predictability. 

Bayesian VAR (BVARs) are based upon the Bayesian Method which is a subjective probability analysis used in a number of different fields. It is a rational decision making regression analysis for updating beliefs. In economics, the methods use monthly, yearly and other time based measurements to help determine the vector and trajectory of actions. It provides a method of blending new information with prior beliefs. 

BVAR models incorporate a greater amount of data than a number of other common models. The authors found that the BVARs have more predictability and would be beneficial for evaluating economic growth. Administrators that consider these models may find an additional tool for understanding and managing economic hubs. 

Gupta, R. & Kabundi, A. (2010). Forecasting macroeconomic variables in small open economy: a comparison between small-and large-scale models. Journal of Forecasting, 29 (2).