Cash Conversion Research Articles

Model for Working Capital Management of Micro, Small and Medium Enterprises in Indonesia by Using Multiple Objective Stochastic Programming

Micro, small and medium enterprises (MSME) have a big influence for the economy in Indonesia. But almost of this company don’t have an optimal working capital management because less knowledge and limitation of financial not only from the owner but also financial credit from bank. Working capital management plays an important role in the success or failure of a company its business activities. To achieve optimal use of working capital management managers the company must accurately control the trade-off between profitability and liquidity. The purpose of this research is to develop a model of working capital management where the sales are under uncertainty and the company has a limit financial. This study was tested using multiple linear regression analysis to see the correlation between profitability and liquidity with x1, x2, x3, x4, and x5 then find optimal solution by stochastic multi objective programming because we consider that sales is in under certainty. The main variables used in the analysis of return on assets to measure profitability, receivable turnover to measure liquidity, inventory turnover, accounts payable days, cash conversion. The results showed that account payable, inventory turnover, and cash turn over have a positive correlation for liquidity but inventory turnover and cash turnover have negative correlation to profitability.

チャネル販売組織のマネジメントコントロール : Cash Conversion Cycleの導入経験から

チャネル販売組織のマネジメントコントロール : Cash Conversion Cycleの導入経験から

Firm's financial risk vulnerability: evidence from Nigeria

This paper examines mean differences in working capital items (liquidity ratio, quick ratio, cash ratio and cash conversion days) and financial leverage of at-risk and not-at-risk firms. The paper also examines association between working capital items and financial leverage. Four hypotheses are tested using data from 2015 online annual financial statements of 89 quoted firms from ten industry sub-sectors classified by the Nigerian Stock Exchange. The data were analysed using paired samples t-tests and OLS multiple regressions. T-test results suggest that there are significant differences in working capital items and financial leverage of not-at-risk and at-risk firms. Comparisons of at-risk and not-at-risk firms suggest that, except for cash ratio, the association between working capital items and financial leverage is not equal for the two groups of firms. Result from the pooled sample suggests that working capital items are significantly associated with financial leverage. The results are not sensitive to industry membership. The pooled model, not-at-risk model and at-risk model explains, respectively, about 67%, 31%, and 94.7% of the variation in financial leverage. The paper recommends that even though firms have enough cash they could still borrow to augment their capital structure.

Accrual Quality: Reversals and Cash Conversion

We estimate the firm-level rate at which working capital accruals convert into future cash flows. These conversion rates determine the expected cash value of a dollar of working capital accruals. For firms whose accrual innovations reverse within one year, we find that, on average, a one dollar innovation to accruals translates into 95 cents of cash flow in the subsequent fiscal year. We find that the relation between working capital accruals and annual returns increases with the rate at which accrual innovations convert to cash flows, as does the relation between accruals and annual bonus pay to the CEO. Moreover, when accrual innovations convert more quickly and completely to cash flows, firms are less likely to receive an Accounting and Auditing Enforcement Release (AAER) from the SEC.

Dead fish across the trail: illustrations of money laundering methods

Uses examples from US case law to illustrate how money laundering has been attempted in the past. Details a large number of specific methods: cash couriers, cash conversion, safe deposit facilities, credit cards, foreign bank accounts, alternative or parallel remittance systems, trusts, precious metal and gems, art and antiques, expensive lifestyles, fictitious loans, real property, cars, boats and planes, stocks and bonds, bearer bonds, casinos, negotiable instruments, cash for cheque exchanges, business, investment funds, non‐profit organisations, shell corporations, and working capital for a criminal enterprise. Also indicates the factors influencing choice of money laundering methods: the amount of money to be laundered, the availability of professional services from people willing to be involved, the scope for intimidation or corruption of professionals, and so on.